research work on computer science

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Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

Latest Research and Reviews

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data

• Aiden Doherty

An efficient polynomial-based verifiable computation scheme on multi-source outsourced data

• Yiran Zhang
• Huizheng Geng

Collective relational inference for learning heterogeneous interactions

Heterogeneous interactions between interactive entities are not well understood due to their complex configurations and many body interactions. Han et al. present a probabilistic-based machine learning method to discover the fundamental laws governing the interactions of heterogeneous systems.

• Zhichao Han
• David S. Kammer

DON6D: a decoupled one-stage network for 6D pose estimation

• Yanwei Zhao

Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification

• Neetu Sigger
• Quoc-Tuan Vien
• Tuan Thanh Nguyen

Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements

• Jimpei Hitsuwari
• Hirohito Okano
• Michio Nomura

News and Comment

Medical artificial intelligence should do no harm

Bias and distrust in medicine have been perpetuated by the misuse of medical equations, algorithms and devices. Artificial intelligence (AI) can exacerbate these problems. However, AI also has potential to detect, mitigate and remedy the harmful effects of bias to build trust and improve healthcare for everyone.

• Melanie E. Moses
• Sonia M. Gipson Rankin

AI hears hidden X factor in zebra finch love songs

Machine learning detects song differences too subtle for humans to hear, and physicists harness the computing power of the strange skyrmion.

• Nick Petrić Howe
• Benjamin Thompson

Three reasons why AI doesn’t model human language

• Johan J. Bolhuis
• Stephen Crain
• Andrea Moro

Generative artificial intelligence in chemical engineering

Generative artificial intelligence will transform the way we design and operate chemical processes, argues Artur M. Schweidtmann.

• Artur M. Schweidtmann

Why scientists trust AI too much — and what to do about it

Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates.

Is ChatGPT making scientists hyper-productive? The highs and lows of using AI

Large language models are transforming scientific writing and publishing. But the productivity boost that these tools bring could have a downside.

• McKenzie Prillaman

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Computer Science (since January 1993)

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Categories within Computer Science

• cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
• cs.CL - Computation and Language ( new , recent , current month ) Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
• cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
• cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
• cs.CG - Computational Geometry ( new , recent , current month ) Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
• cs.GT - Computer Science and Game Theory ( new , recent , current month ) Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
• cs.CV - Computer Vision and Pattern Recognition ( new , recent , current month ) Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
• cs.CY - Computers and Society ( new , recent , current month ) Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
• cs.CR - Cryptography and Security ( new , recent , current month ) Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
• cs.DS - Data Structures and Algorithms ( new , recent , current month ) Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
• cs.DB - Databases ( new , recent , current month ) Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
• cs.DL - Digital Libraries ( new , recent , current month ) Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
• cs.DM - Discrete Mathematics ( new , recent , current month ) Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
• cs.DC - Distributed, Parallel, and Cluster Computing ( new , recent , current month ) Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
• cs.ET - Emerging Technologies ( new , recent , current month ) Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
• cs.FL - Formal Languages and Automata Theory ( new , recent , current month ) Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
• cs.GL - General Literature ( new , recent , current month ) Covers introductory material, survey material, predictions of future trends, biographies, and miscellaneous computer-science related material. Roughly includes all of ACM Subject Class A, except it does not include conference proceedings (which will be listed in the appropriate subject area).
• cs.GR - Graphics ( new , recent , current month ) Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
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• cs.LO - Logic in Computer Science ( new , recent , current month ) Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
• cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
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• cs.NI - Networking and Internet Architecture ( new , recent , current month ) Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
• cs.NE - Neural and Evolutionary Computing ( new , recent , current month ) Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
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• cs.OS - Operating Systems ( new , recent , current month ) Roughly includes material in ACM Subject Classes D.4.1, D.4.2., D.4.3, D.4.4, D.4.5, D.4.7, and D.4.9.
• cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
• cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
• cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
• cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
• cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
• cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
• cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
• cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
• cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.

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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.

The contributions of Cornell Computer Science to research and education are widely recognized, as shown by two Turing Awards, two Von Neumann medals, two MacArthur "genius" awards, and dozens of NSF Career awards our faculty have received, among numerous other signs of success and influence.

To explore current computer science research at Cornell, follow links at the left or below.

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Statistical genetics, sequence analysis, structure analysis, genome assembly, protein classification, gene networks, molecular dynamics

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The theory of computing is the study of efficient computation, models of computational processes, and their limits.

Computer vision

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Join the community, trending research, viplanner: visual semantic imperative learning for local navigation.

This optimization uses a differentiable formulation of a semantic costmap, which enables the planner to distinguish between the traversability of different terrains and accurately identify obstacles.

Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs

In this letter, we address the problem of enabling LLMs to comprehend Area Graph, a text-based map representation, in order to enhance their applicability in the field of mobile robotics.

LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.

Sound Multimedia Audio and Speech Processing

Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles

Fields2Cover/Fields2Cover • 14 Oct 2022

This paper describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles.

Robotics Computational Geometry

BinSym: Binary-Level Symbolic Execution using Formal Descriptions of Instruction Semantics

agra-uni-bremen/binsym • 5 Apr 2024

BinSym is a framework for symbolic program analysis of software in binary form.

Software Engineering Cryptography and Security Programming Languages

OmniNxt: A Fully Open-source and Compact Aerial Robot with Omnidirectional Visual Perception

hkust-aerial-robotics/omninxt • 29 Mar 2024

Adopting omnidirectional Field of View (FoV) cameras in aerial robots vastly improves perception ability, significantly advancing aerial robotics's capabilities in inspection, reconstruction, and rescue tasks.

GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration

tumftm/gmmcalib • 4 Apr 2024

We also address the local minima problem of local registration methods for extrinsic sensor calibration and use a distance-based metric to evaluate the calibration results.

AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario

This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks.

Sound Audio and Speech Processing

Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares

decargroup/hessian_sum_mixtures • 8 Apr 2024

The proposed Hessian approximation is derived by setting the Hessians of the Gaussian mixture component errors to zero, which is the same starting point as for the Gauss-Newton Hessian approximation for NLS, and using the chain rule to account for additional nonlinearities.

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How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2024-2025

• Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
• Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
• Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
• Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

• Machine learning driven design
• Generative models for structured discrete objects
• Approximate inference in probabilistic models
• Accelerating solutions to partial differential equations
• Innovative uses of automatic differentiation
• Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

• Research Areas: Formal methods, programming languages, compilers, computer security.
• Software verification (for which taking COS 326 / COS 510 is helpful preparation)
• Game theory of poker or other games (for which COS 217 / 226 are helpful)
• Computer game-playing programs (for which COS 217 / 226)
•  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

• Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
• Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
• Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
• Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
• Any topic in theoretical computer science.

David August, Room 221

Not available for IW or thesis advising, 2024-2025

• Research Areas: Computer Architecture, Compilers, Parallelism
• Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
• Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
• Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

• Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
• Topics in computational and communication complexity.
• Applications of information theory in complexity theory.
• Algorithms for problems under real-life assumptions.
• Game theory, network effects
• Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

• Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
• Methods for collaborative and continual learning.
• Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

• Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
• Natural algorithms (flocking, swarming, social networks, etc).
• Sublinear algorithms
• Self-improving algorithms
• Markov data structures

Danqi Chen, Room 412

• My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
• Representation learning for text and knowledge bases
• Pre-training and transfer learning
• Question answering and reading comprehension
• Information extraction
• Text summarization
• Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

• Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)
• Research Areas: Machine learning

Jia Deng, Room 423

•  Research Areas: Computer Vision, Machine Learning.
• Object recognition and action recognition
• Deep Learning, autoML, meta-learning
• Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

• Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

• Research Areas:  Software engineering; software engineering education.
• Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
• In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

• Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
• Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

• Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
• Using RL algorithms to applications in science and engineering.
• Emergent behavior of RL algorithms on high-fidelity robotic simulators.
• Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

• Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
• Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
• Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
• Quantitative approaches to theoretical linguistics questions
• Extensions and interfaces for WordNet (English and WN in other languages),
• Applications of WordNet(s), including:
• Foreign language tutoring systems,
• Spelling correction software,
• Word-finding/suggestion software for ordinary users and people with memory problems,
• Machine Translation 
• Sentiment and Opinion detection
• Automatic reasoning and inferencing
• Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

• Networking and telecommunications
• Learning, perception, and intelligence, artificial and otherwise;
• Human-computer interaction and computer-supported cooperative work
• Online education, especially in Computer Science Education
• Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
• Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

• Research Areas: Distributed systems, security, networking
• Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

• Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
• Develop a technique for understanding AI models
• Design a AI model that is interpretable by design
• Build a paradigm for detecting and/or correcting failure points in an AI model
• Analyze an existing AI model and/or dataset to better understand its failure points
• Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
• Develop a software package for explainable AI
• Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

• Research Areas: Formal methods, program analysis, logic decision procedures
• Finding bugs in open source software using automatic verification tools
• Software verification (program analysis, model checking, test generation)
• Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

• Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
• Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
• Implementation and algorithm engineering for control, reinforcement learning and robotics
• Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

• Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
• Optical Neural Networks
• Hardware-in-the-loop Holography
• Zero-shot and Simulation-only Learning
• Object recognition in extreme conditions
• 3D Scene Representations for View Generation and Inverse Problems
• Long-range Imaging in Scattering Media
• Hardware-in-the-loop Illumination and Sensor Optimization
• Inverse Lidar Design
• Phase Retrieval Algorithms
• Proximal Algorithms for Learning and Inference
• Domain-Specific Language for Optics Design

Peter Henderson , 302 Sherrerd Hall

• Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

• Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
• See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

• Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
• Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
• Software visualization tools for education
• Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

• Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
• Application-oriented languages, scripting languages.
• Tools; user interfaces
• Digital humanities

Zachary Kincaid, Room 219

• Research areas: programming languages, program analysis, program verification, automated reasoning
• Independent Research Topics:
• Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
• Design a domain-specific programming language, or prototype a new feature for an existing language.
• Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

• Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Pravesh Kothari, Room 320

• Research areas: Theory

Amit Levy, Room 307

• Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
• Distributed hardware testing infrastructure
• Second factor security tokens
• Low-power wireless network protocol implementation
• USB device driver implementation

Kai Li, Room 321

• Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
• Fast communication mechanisms for heterogeneous clusters.
• Approximate nearest-neighbor search for high dimensional data.
• Data analysis and prediction of in-patient medical data.
• Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

• Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
• Explore new statistical retrieval models for document retrieval and question answering.
• Apply AI in various fields.
• Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
• Any interesting project related to AI, machine learning, and data analysis.

Lydia Liu, Room 414

• Research Areas: algorithmic decision making, machine learning and society
• Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
• Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
• Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
• Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

Wyatt Lloyd, Room 323

• Research areas: Distributed Systems
• Caching algorithms and implementations
• Storage systems
• Distributed transaction algorithms and implementations

Alex Lombardi , Room 312

• Research Areas: Theory

Margaret Martonosi, Room 208

• Quantum Computing research, particularly related to architecture and compiler issues for QC.
• Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
• Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
• Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

Available for Spring 2025 single-semester IW, only

• Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
• Assessing the effects of government policies, both in the public and private sectors.
• Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
• Developing new tools to improve government processes and offer policy alternatives.

Mae Milano, Room 307

• Local-first / peer-to-peer systems
• Wide-ares storage systems
• Consistency and protocol design
• Type-safe concurrency
• Language design
• Gradual typing
• Domain-specific languages
• Languages for distributed systems

Andrés Monroy-Hernández, Room 405

• Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
• Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
• Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

• Research areas: Distributed systems, high-throughput computing, computer science/engineering education
• Expansion, improvement, and evaluation of open-source distributed computing software.
• Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
• Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
• Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

• Research areas: Natural Language Processing, Reinforcement Learning
• Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
• Transfer learning/generalization in NLP
• Techniques for generating natural language
• Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

• Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
• Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
• Possible research topics include:

1.   Quantum algorithms and circuits:

• i. Design or simulation quantum circuits implementing quantum algorithms.
• ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

• i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
• ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
• iii. Necessary and sufficient conditions for tractability of Weighted problems.
• iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

• i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

• Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
• Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

• Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
• Implementation and application of algorithms to infer evolutionary processes in cancer
• Identifying correlations between combinations of genomic mutations in human and cancer genomes
• Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
• Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

• Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
• Constructing a new method to explain a model / create an interpretable by design model
• Analyzing a current model / dataset to understand bias within the model/dataset
• Proposing new fairness evaluations
• Proposing new methods to train to improve fairness
• Developing synthetic datasets for fairness / interpretability benchmarks
• Understanding robustness of models

Ran Raz, Room 240

• Research Area: Computational Complexity
• Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

• Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
• Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
• Investigate approaches to robust 3D scan matching
• Model and compensate for imperfections in 3D printing
• Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

• Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
• Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
• Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
• Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

• Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
• Gamification of neuroscience (EyeWire  2.0)
• Semantic segmentation and object detection in brain images from microscopy
• Computational analysis of brain structure and function
• Neural network theories of brain function

Jaswinder Pal Singh, Room 324

• Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
• Develop a startup company idea, and build a plan/prototype for it.
• Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
• Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
• Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
• Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

• Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
• Whole and cross-genome methods for predicting protein function and protein-protein interactions.
• Analysis and prediction of biological networks.
• Computational methods for inferring specific aspects of protein structure from protein sequence data.
• Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

• Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
• Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
• Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

• Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
• Implement and evaluate one or more gene expression analysis algorithm.
• Develop algorithms for assessment of performance of genomic analysis methods.
• Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

• Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
• Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

• Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

• Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
• Design and implement computer visualizations of algorithms or data structures.
• Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
• Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

• Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
• Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
• Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

• data structures
• streaming algorithms
• design and analyze data structures / streaming algorithms
• prove impossibility results (lower bounds)
• implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

Opportunities outside the department.

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

• Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

• Documentation of historic structures
• Cyber physical systems for structural health monitoring
• Developing virtual and augmented reality applications for documenting structures
• Applying machine learning techniques to generate 3D models from 2D plans of buildings
•  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

• Internet security and privacy 
• Social Networks
• Privacy technologies, anonymous communication
• Network Science
• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

• Research Areas: Memory, the brain and computation 
• Lab:  Princeton Computational Memory Lab

Potential research topics

• Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
• Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

• Apply game theory to sustainability challenges
• Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
• How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

• Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
• Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

• Instrument Princeton's Green (HPCRC) data center
• Investigate power utilization on an processor core implemented in an FPGA
• Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
• Other topics in parallel computer architecture or operating systems

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Computer architecture, educational technology.

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Graphics and Vision

Human-computer interaction.

• Information Science and Systems
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• Natural Language and Speech Processing
• Optics + Photonics
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Programming Languages and Software Engineering

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Theory of Computation

• Departmental History
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• Explore all research areas

Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

Latest news

Engineering household robots to have a little common sense.

With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.

New software enables blind and low-vision users to create interactive, accessible charts

Screen-reader users can upload a dataset and create customized data representations that combine visualization, textual description, and sonification.

AI generates high-quality images 30 times faster in a single step

Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.

Optimizing nuclear fuels for next-generation reactors

While working to nurture scientific talent in his native Nigeria, Assistant Professor Ericmoore Jossou is setting his sights on using materials science and computation to design robust nuclear components.

2024 MacVicar Faculty Fellows named

Professors Berggren, Campbell, Pollock, and Vaikuntanathan are honored for exceptional undergraduate teaching.

Upcoming events

Fireside chat with angel investor helen he, doctoral thesis: enhancing online collaborative learning: designs for effective in-situ discussion and engagement in large-scale learning environments., adriaan campo – exploring kinematic parameters in music education and performance: towards real-time feedback and motor learning assessment, doctoral thesis:  advancing equity & reliability in machine learnring, doctoral thesis: data-efficient machine learning for computational imagingdoctoral thesis:, doctoral thesis: automated interpretation of machine learning models.

Email forwarding for @cs.stanford.edu is changing. Updates and details here . Looking for your W-2 form?  Read Here . CS Commencement Ceremony June 16, 2024.  Learn More .

BS | Research Opportunities

Main navigation.

The Computer Science Department at Stanford have faculty and students that are globally recognized for their innovative and cutting-edge research. We offer scholars various opportunities at their disposal to participate in undergraduate research. If you are interested in research, we welcome you to explore the opportunities at your disposal.

CURIS Research

The program for CS undergrad Summer research. Participating students will work on their projects full-time and are paid a stipend for living expenses. 

Independent Study

Undergraduate research is often done through CURIS, for academic credit, or through an informal arrangement with a professor.

Getting Started

• Undergraduate CS research website . The most reliable way to learn about projects you can get involved in is through the  undergraduate CS research  website. Throughout the year, professors have openings for undergrads to do work in their labs. They post descriptions of these projects on the site for your perusal. This site lists CS research projects during the academic year for course credit, CS research projects for the Summer quarter under CURIS (paid internship), and research projects in other departments that include CS applications.
• Go to office hours . Find a professor whose research interests you want to learn more about. Discuss what possibilities are available or find out more about a particular group. Often the professor will be able to direct you to some research papers that might be valuable to read or other groups that you might find interesting. It's always a good idea to email a professor and let them know that you will be coming in. That way if their office hours are particularly busy, they can suggest another time.
• Connect with a graduate student . Graduate students work on projects every day and deal with most of the details, they are probably one of the best sources of information. They will have a good idea of what role you could initially play in the project and will also be able to give an honest assessment of what it is like to work with the professor and what are the expectations of the group. Finally, if you decide to work with the group, the graduate students will probably be the ones who will be mentoring you in the day-to-day aspects of your work. Before you choose a project, try to meet with at least one graduate student in the group, preferably one that would be mentoring you. If you are still deciding between projects, ask the graduate students for their opinion.
• Read your email . The bscs list is constantly getting announcements about presentations that are being given by faculty, advanced graduate students, and visiting faculty. Take the time to read through some of the abstracts and pick a few that interest you. These announcements are not usually forwarded to the considering_cs list. If you are interested in getting these announcements, visit the  course advisor  and declare CS !
• CURIS poster sessions . At the end of the Summer quarter and the beginning of the Fall quarter, the CURIS program organizes poster sessions for undergraduates to present their Summer research projects. This is a great opportunity for you to get first-hand information about your peers' research experience as well as potential project ideas and research groups of interest. In addition, the display in the Gates lobby shows a collection of both undergraduate and graduate research projects year-round.
• 500 level seminars . All of the CS 500 level courses are topic seminars. For instance,  CS 547 Seminar  focuses on Human-Computer Interaction topics. Each week, a different speaker comes in and presents their research. Sometimes the speakers are Stanford professors, graduate students, or they're outside visitors. The presentations are technical, check the schedules on the class web pages to find talks that may be interesting.
• CS300 ( speaker schedule ) . At the beginning of each academic year, all new PhD students are required to take CS 300. In each seminar, two professors come in and describe their research work. The idea is to give PhD students an overview of the ongoing research so they can decide which groups they would like to join. Although the class is technically for PhD students, undergraduate and Master's students can enroll. The presentations are likely to be somewhat technical, but since they are geared toward PhD students with a broad variety of interests, they should be fairly accessible.

Research in the Computer Science Department encompasses the foundations, and explores the frontiers of computing science.

• Faculty Research Guide

Our Faculty Research Guide (FRG) is a good starting point to see which faculty are working on some of today's most interesting research challenges.

The work we do here covers a wide range of research interests. Each faculty member has written his or her own research summary, which is available on their individual CSD directory page.

CSD PhD Blog

Visit the CSD PhD Blog for articles on some recent research being done by our doctoral students. 

Undergraduate Research

Undergraduate independent research in Computer Science is done as an Independent Study or as a Senior Thesis (which typically grows out of a prior Independent Study experience).

• Undergraduate Research Options

• Artificial Intelligence

• Programming Languages

CSD Doctoral Theses

Searchable list of Computer Science Department Doctoral Theses

CSD Technical Reports

2023:  Technical Reports  |  Master's & Doctoral Theses by Author

2022:  Technical Reports  | Master's & Doctoral Theses by Author

2021:  Technical Reports  | Master's & Doctoral Theses by Author

All CSD Technical Reports

• Student intranet /
• Staff intranet

Department of Computer Science

Research projects

Find a postgraduate research project in your area of interest by exploring the research projects that we offer in the Department of Computer Science.

We have a broad range of research projects for which we are seeking doctoral students. Browse the list of projects on this page or follow the links below to find information on doctoral training opportunities, or applying for a postgraduate research programme.

• Doctoral training opportunities
• How to apply

Alternatively, if you would like to propose your own project then please include a research project proposal and the name of a possible supervisor with your application.

Available projects

List by research theme List by supervisor

Future computing systems projects

• A Multi-Tenancy FPGA Cloud Infrastructure and Runtime System
• A New Generation of Terahertz Emitters: Exploiting Electron Spin
• Balancing security and privacy with data usefulness and efficiency in wireless sensor networks
• Blockchain-based Local Energy Markets
• Cloud Computing Security
• Design and Exploration of a Memristor-enabled FPGA Architecture
• Design and Implementation of an FPGA-Accelerated Data Analytics Database
• Designing Safe & Explainable Neural Models in NLP
• Dynamic Resource Management for Intelligent Transportation System Applications
• Evaluating Systems for the Augmentation of Human Cognition
• Exploring Unikernel Operating Systems Running on reconfigurable Softcore Processors
• Finding a way through the Fog from the Edge to the Cloud
• Guaranteeing Reliability for IoT Edge Computing Systems
• Hardware Aware Training for AI Systems
• Hybrid Fuzzing Concurrent Software using Model Checking and Machine Learning
• Job and Task Scheduling and Resource Allocation on Parallel/Distributed systems including Cloud, Edge, Fog Computing
• Machine Learning with Bio-Inspired Neural Networks
• Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
• Pervasive Technology for Multimodal Human Memory Augmentation
• Power Management Methodologies for IoT Edge Devices
• Power Transfer Methods for Inductively Coupled 3-D ICs
• Problems in large graphs representing social networks
• Programmable Mixed-Signal Fabric for Machine Learning Applications
• Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
• Security and privacy in p2p electricity trading
• Skyrmion-based Electronics
• Smart Security for Smart Services in an IoT Context
• Spin waves dynamics for spintronic computational devices
• Technology-driven Human Memory Degradation
• Ultrafast spintronics with synthetic antiferromagnets

Human centred computing projects

• Advising on the Use and Misuse of Collaborative Coding Workflows
• Automatic Activity Analysis, Detection and Recognition
• Automatic Emotion Detection, Analysis and Recognition
• Automatic Experimental Design with Human in the Loop
• Biases in Physical Activity Tracking
• Computer Graphics - Material Appearance Modeling and Physically Based Rendering
• Design principles for glancing at information by visually disabled users
• Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
• Geo-location as a Predictor of Type 1 Diabetes Blood Glucose
• Learning of user models in human-in-the-loop machine learning
• Machine Learning and Cognitive Modelling Applied to Video Games
• Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
• Music Generation and Information Processing via Deep Learning
• Understanding the role of the Web on Memory for Programming Concepts
• User Modeling for Physical Activity Tracking

Artificial intelligence projects

• (MRC DTP) Unlocking the research potential of unstructured patient data to improve health and treatment outcomes
• Abstractive multi-document summarisation
• Applying Natural Language Processing to real-world patient data to optimise cancer care
• Automated Repair of Deep Neural Networks
• Automatic Learning of Latent Force Models
• Biologically-Plausible Continual Learning
• Cognitive Robotics and Human Robot Interaction
• Collaborative Probabilistic Machine Learning
• Computational Modelling of Child Language Learning
• Contextualised Multimedia Information Retrieval via Representation Learning
• Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning
• Data Integration & Exploration on Data Lakes
• Data Lake Exploration with Modern Artificial Intelligence Techniques
• Data-Science Approaches to Better Understand Multimorbidity and Treatment Outcomes in Patients with Rheumatoid Arthritis
• Deep Learning for Temporal Information Processing
• Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
• Event Coreference at Document Level
• Explainable and Interpretable Machine Learning
• Formal Verification for Robot Swams and Wireless Sensor Networks
• Formal Verification of Robot Teams or Human Robot Interaction
• Foundations and Advancement of Subontology Generation for Clinically Relevant Information
• Generating Goals from Responsibilities for Long Term Autonomy
• Generating explainable answers to fact verification questions
• Integrated text and table mining
• Interpretable machine learning for healthcare applications
• Knowledge Graph Construction via Learning and Reasoning
• Knowledge Graph for Guidance and Explainability in Machine Learning
• Machine Learning for Vision and Language Understanding
• Multi-task Learning and Applications
• Neuro-sybolic theorem proving
• Ontology Informed Machine Learning for Computer Vision
• Optimization and verification of systems modelled using neural networks
• Probabilistic modelling and Bayesian machine learning
• Representation Learning and Its Applications
• Software verification with contrained Horn clauses and first-order theorem provers
• Solving PDEs via Deep Neural Nets: Underpinning Accelerated Cardiovascular Flow Modelling with Learning Theory
• Solving mathematical problems using automated theorem provers
• Solving non-linear constraints over continuous functions
• Symmetries and Automated Theorem Proving
• Text Analytics and Blog/Forum Analysis
• Theorem Proving for Temporal Logics
• Trustworthy Multi-source Learning
• Verification Based Model Extraction Attack and Defence for Deep Neural Networks
• Zero-Shot Learning and Applications

Software and e-infrastructure projects

• Automatic Detection and Repair of Software Vulnerabilities in Unmanned Aerial Vehicles
• Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things
• Component-based Software Development.
• Effective Teaching of Programming: A Detailed Investigation
• Exploiting Software Vulnerabilities at Large Scale
• Finding Vulnerabilities in IoT Software using Fuzzing, Symbolic Execution and Abstract Interpretation
• Using Program Synthesis for Program Repair in IoT Security
• Verifying Cyber-attacks in CUDA Deep Neural Networks for Self-Driving Cars

Theory and foundations projects

• Application Level Verification of Solidity Smart Contracts
• Categorical proof theory
• Formal Methods: Hybrid Event-B and Rodin
• Formal Methods: Mechanically Checking the Semantics of Hybrid Event-B
• Formal Semantics of the Perfect Language
• Mathematical models for concurrent systems

James Elson projects

Data science projects.

• Data Wrangling
• Fishing in the Data Lake
• Specifying and Optimising Data Wrangling Tasks

Sophia Ananiadou projects

Mauricio alvarez projects, richard banach projects, riza batista-navarro projects, ke chen projects, sarah clinch projects, angelo cangelosi projects, jiaoyan chen projects, lucas cordeiro projects, louise dennis projects, clare dixon projects, suzanne embury projects, marie farrell projects, alejandro frangi projects, andre freitas projects, michael fisher projects, gareth henshall projects, simon harper projects, caroline jay projects, samuel kaski projects, dirk koch projects, konstantin korovin projects, kung-kiu lau projects, zahra montazeri projects, christoforos moutafis projects, tingting mu projects, anirbit mukherjee projects, mustafa mustafa projects, goran nenadic projects, paul nutter projects, nhung nguyen projects, pierre olivier projects, norman paton projects, vasilis pavlidis projects, pavlos petoumenos projects, steve pettifer projects, oliver rhodes projects, giles reger projects, rizos sakellariou projects, uli sattler projects, andrea schalk projects, renate schmidt projects, robert stevens projects, sandra sampaio projects, viktor schlegel projects, youcheng sun projects, tom thomson projects, junichi tsujii projects, markel vigo projects, ning zhang projects, liping zhao projects, hongpeng zhou projects.

Find Info For

• Become a Student
• Current Students
• Research and Partnerships

Quick Links

Departmental Research Areas

• Research Centers and Institutes
• Current Department Administered Research Funding
• Research Seminars
• Technical Reports

In the past five years, Computer Science faculty have had research collaborations with every other college at Purdue. The work of the computer scientist is applicable just about everywhere. Though research activity spans many broad areas, the list below reflects the interests and expertise of the faculty summarized in 14 areas.

Artificial Intelligence, Machine Learning, and Natural Language Processing

Our group members study and devise core machine learning and artificial intelligence methods to solve complex problems throughout science, engineering, and medicine. Our goal is to enhance human lives and bring advanced technologies to augment human capabilities. This research involves both deployments in real-world applications as well as development of fundamental theories in computer science, mathematics, and statistics.  

List of Faculty

• Aniket Bera
• Simina Branzei
• Brian Bullins*
• Berkay Celik
• Chris Clifton
• David Gleich
• Dan Goldwasser*
• Steve Hanneke*
• Jean Honorio*
• Sooyeon Jeong
• Rajiv Khanna*
• Anuran Makur
• Jennifer Neville*
• Chunyi Peng
• Alex Psomas
• Ahmed Qureshi*
• Bruno Ribeiro*
• Tiark Rompf
• Muhammad Shahbaz
• Paul Valiant
• Jianguo Wang
• Yexiang Xue*
• Raymond Yeh*
• Ruqi Zhang*
• Tianyi Zhang

(* indicates primary area of research)

Related Links

• Co gnitive  R obot  A utonomy and  L earning (CoRAL) lab
• MINDS: Data Science, Machine Learning, and AI
• PurPL: Center for Programming Principles and Software Systems

Bioinformatics and Computational Biology

Faculty in the area of bioinformatics and computational biology apply computational methodologies such as databases, machine learning, discrete, probabilistic, and numerical algorithms, and methods of statistical inference to problems in molecular biology, systems biology, structural biology, and molecular biophysics.

• Bedrich Benes
• Petros Drineas
• Ananth Grama
• Majid Kazemian*
• Daisuke Kihara*
• Alex Pothen
• Wojtek Szpankowski
• Kihara Bioinformatics Lab
• Kazemian Lab

Sample Projects

• PrFEcT-Predict
• 3D-SURFER 2.0
• Alex Pothen Software Artifacts
• Majid Kazemian Software Artifacts

Computer Architecture

Computer Architecture research studies the interplay between computer hardware and software, particularly at the intersection of programming languages, compilers, operating systems, and security.

• Changhee Jung
• Xuehai Qian*
• Kazem Taram*

Computational Science and Engineering

The research area of Computational Science and Engineering answers questions that are too big to address experimentally or are otherwise outside of experimental abilities. Using the latest computers and algorithms, this group addresses those questions through numerical modeling and analysis, high-performance computation, massive distributed systems, combinatorial algorithms in science applications, high-speed data analysis, and matrix-based computations for numerical linear algebra.

• Petros Drineas*
• David Gleich*
• Ananth Grama*
• Alex Pothen*
• Ahmed Qureshi
• Elisha Sacks
• Xavier Tricoche
• Yexiang Xue

CSE Research Group

• David Gleich Software Artifacts
• Finite Element Analysis of 9/11 Attacks

Databases and Data Mining

The data revolution is having a transformational impact on society and computing technology by making it easier to measure, collect, and store data. Our databases and data mining (big data) research group develops models, algorithms, and systems to facilitate and support data analytics in large-scale, complex domains.  Application areas include database privacy and security, web search, spatial data, information retrieval, and natural language processing.

• Walid Aref*
• Elisa Bertino
• Bharat Bhargava*
• Chris Clifton*
• Dan Goldwasser
• Susanne Hambrusch
• Jennifer Neville
• Sunil Prabhakar*
• Bruno Ribeiro
• Jianguo Wang*
• Cyber Space Security Lab (CyberS2Lab)
• Conceptual Evaluation and Optimization of Queries in Spatiotemporal Data Systems
• Secure Dissemination of Video Data in Vehicle-to-Vehicle Systems
• Ensuring Integrity and Authenticity of Outsourced Databases
• Towards Scalable and Comprehensive Uncertain DAta Management
• ORION DBMS: Handling Nebulous Data

Distributed Systems

The DS group focuses on designing distributed systems that are scalable, dependable, and secure, behaving according to their specification in spite of errors, misconfigurations, or being subjected to attacks. Areas of focus include virtualization technologies with emphasis on developing advanced technologies for computer malware defense and cloud computing.

• Bharat Bhargava
• Pedro Fonseca
• Suresh Jagannathan
• Aniket Kate
• Kihong Park
• Vernon Rego*
• Eugene Spafford
• Yongle Zhang
• Vassilis Zikas
• Saurabh Bagchi (by courtesy)
• Charlie Hu (by courtesy)
• Sanjay Rao (by courtesy)

  (* indicates primary area of research)

• Dependable Computing Systems Lab
• FRIENDS Lab
• ProTracer: Practical Provenance Tracing
• DCSL Projects

Graphics and Visualization

This group performs research in graphics, visualization, computational geometry, and related applications.  Focus areas include model acquisition, image generalization, scientific visualization, urban modeling, robust computational geometry, and geometric computations and constraints.

• Daniel Aliaga *
• Bedrich Benes*
• Voicu Popescu*
• Elisha Sacks*
• Xavier Tricoche*
• Computer Graphics and Visualization Lab
• High Performance Computer Graphics Laboratory

Graphics Lab Projects

Human-Computer Interaction

• Sooyeon Jeong*
• Tianyi Zhang*

Information Security and Assurance

Strong security and privacy is needed to defend our records, communications, finances, governments and infrastructure against all manner of threats and attacks, while also enhancing legitimate uses. Research in Information Security and Assurance focuses on the analysis, development, and deployment of technologies, algorithms, and policies to protect computing and data resources against malicious access or tampering, and to validate authenticity. 

• Mikhail Atallah*
• Elisa Bertino*
• Antonio Bianchi*
• Jeremiah Blocki*
• Berkay Celik*
• Sonia Fahmy
• Christina Garman*
• Aniket Kate*
• Ninghui Li*
• Hemanta Maji*
• Sunil Prabhakar
• Vernon Rego
• Eugene Spafford*
• Dongyan Xu*
• Vassilis Zikas*
• Freedom Research Lab
• Database Security Lab
• Spatial-temporal Recreation of Android App Displays from Memory Images
• Multiple Perspective Attack Investigation with Semantic Aware Execution Partitioning
• HexHive Group Projects
• Chunyi Peng Mobile Phone Projects
• Freedom Lab Projects

Networking and Operating Systems

This area works on fundamental problems at different layers of the network protocol stack – from the medium access control layer up to the application layer – using theoretical models, simulation, emulation, and extensive testbed experimentation to develop and evaluate proposed solutions which leverage techniques from game theory, information theory, complexity theory, optimization, and cryptography.

• Saurabh Bagchi*
• Antonio Bianchi
• Doug Comer*
• Sonia Fahmy*
• Pedro Fonseca*
• Kihong Park*
• Chunyi Peng*
• Muhammad Shahbaz*
• Yongle Zhang*

Programming Languages and Compilers

The PL group engages in research spanning all aspects of software systems design, analysis, and implementation.  Active research projects exist in functional and object-oriented programming languages, both static and dynamic compilation techniques for scalable multicore systems, generative programming, assured program generation, scripting languages, distributed programming abstractions and implementations, real time and embedded systems, mobile and untrusted computing environments, and runtime systems with special focus on memory management and parallel computing environments.

• Ben Delaware*
• Suresh Jagannathan*
• Changhee Jung*
• Zhiyuan Li*
• Ryan Newton*
• Tiark Rompf*
• Roopsha Samanta*
• Xiangyu Zhang*
• Yung-Hsiang Lu (by courtesy)
• Milind Kulkarni (by courtesy)
• PurForM  - Purdue's Formal Methods research group

PurPL - Center for Programming Principles and Software Systems

• Secure Software Systems Lab (S3)

Software Engineering

The software engineering area conducts research on applying advanced program analyses towards problems related to fault isolation and various kinds of bug detection, including those related to race conditions in concurrent programs, and specification inference for large-scale software systems.

• Ben Delaware
• Buster Dunsmore*
• Xiangyu Zhang

Automatic Model Generation from Documentation for Java API Functions

Robotics and Computer Vision

The Robotics and Computer Vision area includes elements of machine learning, signal processing, and image processing to further develop robotics and computer vision systems from a computational science perspective.

• Aniket Bera*
• Raymond Yeh

Theory of Computing, Algorithms, and Quantum Computing

Members of the group work in areas that include analysis of algorithms, parallel computation, computational algebra and geometry, computational complexity theory, digital watermarking, data structures, graph algorithms, network algorithms, distributed computation, information theory, analytic combinatorics, random structures, external memory algorithms, and approximation algorithms.

• Mikhail Atallah
• Saugata Basu*
• Jeremiah Blocki
• Simina Branzei*
• Brian Bullins
• Elena Grigorescu*
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On computer science research and its temporal evolution

• Published: 30 July 2022
• Volume 127 , pages 4913–4938, ( 2022 )

Cite this article

• Camil Demetrescu   ORCID: orcid.org/0000-0002-4686-6745 1 ,
• Irene Finocchi   ORCID: orcid.org/0000-0002-6394-6798 2 ,
• Andrea Ribichini   ORCID: orcid.org/0000-0002-0281-4257 1 &
• Marco Schaerf   ORCID: orcid.org/0000-0002-2016-1966 1  

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In this article, we study the evolution of the computer science research community over the past 30 years. Analyzing data from the full Scopus database, we investigate how aspects such as the community size, gender composition, and academic seniority of its members changed over time. We also shed light on the varying popularity of specific research areas, as derived from the ACM’s Special Interest Groups and IEEE classifications. Our analysis spans 19 nations (all members of the G20 group, excluding the EU) and involves a total of 728,374 authors and 8,412,543 publications. This work shows that the overall size of the computer science community has increased by a factor of ten in the time period 1991–2020, with China and India enjoying the highest growth. At the same time, this increase has not been uniform across research areas. Female participation has also increased, but more slowly than expected and not uniformly across countries and areas.

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Prof. Demetrescu, Prof. Finocchi and Dr. Ribichini were partially supported for this work by MIUR, the Italian Ministry of Education, University and Research, under PRIN Project n. 20174LF3T8 AHeAD (Efficient Algorithms for HArnessing Networked Data).

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Demetrescu, C., Finocchi, I., Ribichini, A. et al. On computer science research and its temporal evolution. Scientometrics 127 , 4913–4938 (2022). https://doi.org/10.1007/s11192-022-04445-z

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Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer.  The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).  

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

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In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project. 

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

• your academic interests or concentration may have changed and thus the lab project is no longer appropriate
• you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
• your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
• the project may not be working and the lab hasn’t offered an alternative
• or there may be personal reasons for leaving.  It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help.  Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

• One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress. 
• On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours. 
• In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab.  This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
• There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
• Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

• For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research. 
• For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
• For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted. 

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

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500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

• Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
• Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
• Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
• Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
• Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

Top 10 Computer Science Research Paper Topics

1. battery life and energy storage for 5g equipment.

The 5G network is an upcoming cellular network with much higher data rates and capacity than the current 4G network. According to research published in the European Scientific Institute Journal, one of the main concerns with the 5G network is the high energy consumption of the 5G-enabled devices . Hence, this research on this topic can highlight the challenges and proffer unique solutions to make more energy-efficient designs.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Other Examples of Computer Science Research Topics & Questions

Computer research topics.

• The confluence of theoretical computer science, deep learning, computational algorithms, and performance computing
• Exploring human-computer interactions and the importance of usability in operating systems
• Predicting the limits of networking and distributed systems
• Controlling data mining on public systems through third-party applications
• The impact of green computing on the environment and computational science

Computer Research Questions

• Why are there so many programming languages?
• Is there a better way to enhance human-computer interactions in computer-aided learning?
• How safe is cloud computing, and what are some ways to enhance security?
• Can computers effectively assist in the sequencing of human genes?
• How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

• Will it be possible to build artificial emotional intelligence?
• Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
• Can artificial intelligence systems self-improve with knowledge from the Internet?

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How to Start a Research Work in Computer Science: A Framework For Beginners

Research is one of the key factors behind the improvement and evolution of any subject in the world. However, the skills to perform the research are rarely taught in the school or during the undergraduate courses. This paper provides a practical and efficient framework or method called 'Eight-Step Approach to Research', which will guide you to learn 'how to start doing research' in a particular area of computer science. Although this paper is meant for students and researchers in computer science but it should be kept in mind that this methodology can be applied to any research area in any field of study.

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This research covers the different sides of a problem that the female students of English Department at the Faculty of Arts, Al-Asmariya Islamic University in Zliten, Libya, suffer when doing their graduate researches. In fact, there are many problems on the way. The main problem is that their experience in writing is poor as there is no real practice in the courses of writing. The absence of a real academic library is another problem. Creativity in the nature of the studies conducted by the students is something that could be far from attention. The university and of course the faculty has to pay more attention to the library and provide it with the necessary references and books in different fields of language and of course in other fields. Another point worth to mention is the absence of the electronic library which may make it easier for the students to have access to international libraries. The researcher tries to probe different types of problems relating to this issue. At the end of the research are the results followed by a list of recommendations.

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How to Start a Research Work in Computer Science: A Framework For Beginners

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Research is one of the key factors behind the improvement and evolution of any subject in the world. However, the skills to perform the research are rarely taught in the school or during the undergraduate courses. This paper provides a practical and efficient framework or method called ‘Eight-Step Approach to Research’, which will guide you to learn ‘how to start doing research’ in a particular area of computer science. Although this paper is meant for students and researchers in computer science but it should be kept in mind that this methodology can be applied to any research area in any field of study.

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What does a computer and information research scientist do?

Would you make a good computer and information research scientist? Take our career test and find your match with over 800 careers.

What is a Computer and Information Research Scientist?

Computer and information research scientists conduct advanced research and studies in the field of computer science, information technology, and related areas. They explore new possibilities in computer hardware and software, algorithms, data analysis, artificial intelligence, and other emerging technologies. They may specialize in areas such as machine learning, cybersecurity, data mining, computer graphics, or networking.

Computer and information research scientists publish research papers, present at conferences, and contribute to the scientific community's knowledge and understanding of computer science. Their research findings and discoveries contribute to the development of new products, technologies, and applications that can impact various industries, such as healthcare, finance, communications, and entertainment.

What does a Computer and Information Research Scientist do?

Computer and information research scientists play an important role in driving technological innovation and shaping the future of computing by exploring new frontiers, solving complex problems, and advancing the field through their research efforts.

Duties and Responsibilities Here are some common responsibilities associated with the role of a computer and information research scientist:

• Research and Experimentation: Conducting advanced research and experimentation to explore new ideas, technologies, and approaches within the field of computer science. This involves formulating research questions, designing experiments, collecting and analyzing data, and drawing conclusions based on the results.
• Technology Development: Developing new technologies, algorithms, models, or software solutions to address complex problems and push the boundaries of computer science. This includes designing innovative systems, architectures, or methodologies that can improve computer performance, efficiency, security, or user experience.
• Data Analysis and Modeling: Analyzing large datasets, applying statistical techniques, and developing models to gain insights, predict trends, or solve specific problems. This involves utilizing techniques such as machine learning, data mining, or data visualization to extract meaningful information and make informed decisions.
• Software and Algorithm Design: Designing and developing software applications, algorithms, or programming languages that enable new functionalities or solve specific computational challenges. This includes writing code, debugging, testing, and optimizing software to ensure its efficiency, reliability, and scalability.
• Collaboration and Communication: Collaborating with other researchers, engineers, and professionals in interdisciplinary teams to exchange ideas, share knowledge, and work towards common goals. Effective communication skills are essential for presenting research findings, writing scientific papers, and delivering presentations at conferences or seminars.
• Technology Evaluation and Assessment: Evaluating existing technologies, systems, or methodologies to identify their strengths, weaknesses, and potential improvements. This involves staying abreast of the latest advancements in the field, assessing their relevance, and providing recommendations for their implementation or refinement.
• Project Management: Planning, organizing, and managing research projects, including setting objectives, allocating resources, and ensuring timely completion of tasks. This may involve supervising and mentoring junior researchers, coordinating collaborations with external partners, and overseeing the overall progress of the project.
• Publication and Knowledge Sharing: Publishing research findings in academic journals, presenting at conferences, and contributing to the scientific community's knowledge base. This includes writing research papers, participating in peer reviews, and staying actively engaged in professional networks and forums.
• Ethical Considerations: Adhering to ethical guidelines and principles in research, particularly when working with sensitive data, artificial intelligence, or human subjects. Ensuring that research practices comply with legal and ethical standards is crucial for maintaining integrity and accountability in the field.

Types of Computer and Information Research Scientists Here are some common types of computer and information research scientists based on their specializations:

• Artificial Intelligence (AI) Research Scientist: Specializes in the development and advancement of AI technologies, including machine learning, natural language processing, computer vision, and robotics. They focus on creating intelligent systems that can learn, reason, and perform tasks autonomously.
• Data Scientist : Focuses on analyzing and interpreting large datasets to extract insights, identify patterns, and make data-driven decisions. They utilize statistical and computational techniques, as well as machine learning algorithms, to uncover meaningful information from complex data.
• Network Research Scientist: Specializes in the design, development, and optimization of computer networks. They focus on areas such as network protocols, network security, network performance analysis, and the development of innovative networking technologies.
• Security Research Scientist: Concentrates on researching and developing techniques to protect computer systems, networks, and data from cyber threats. They work on areas such as cryptography, secure software development, intrusion detection, vulnerability analysis, and security protocols.
• Human-Computer Interaction (HCI) Research Scientist: Studies the interaction between humans and computer systems, with a focus on improving user experience, usability, and accessibility. They investigate user behavior, design intuitive interfaces, and develop interactive technologies that better meet users' needs.
• Computer Graphics and Visualization Research Scientist: Specializes in the development and enhancement of computer graphics algorithms, 3D modeling, virtual reality, augmented reality, and data visualization techniques. They work on creating visually compelling and interactive computer-generated imagery.
• Software Engineering Research Scientist: Concentrates on advancing software development methodologies, tools, and practices. They research software architecture, software testing, software quality assurance, and other areas to improve the efficiency, reliability, and maintainability of software systems.
• Natural Language Processing (NLP) Research Scientist: Focuses on understanding and processing human language by computers. They work on tasks such as machine translation, sentiment analysis, information retrieval, and automated speech recognition to enable computers to understand and generate human language.
• Quantum Computing Research Scientist: Specializes in the field of quantum computing, which involves developing algorithms, designing quantum circuits, and exploring the potential applications of quantum technologies. They work on harnessing the power of quantum mechanics to solve complex computational problems.

Are you suited to be a computer and information research scientist?

Computer and information research scientists have distinct personalities . They tend to be investigative individuals, which means they’re intellectual, introspective, and inquisitive. They are curious, methodical, rational, analytical, and logical. Some of them are also artistic, meaning they’re creative, intuitive, sensitive, articulate, and expressive.

Does this sound like you? Take our free career test to find out if computer and information research scientist is one of your top career matches.

What is the workplace of a Computer and Information Research Scientist like?

The workplace of a computer and information research scientist can vary depending on their specific role, employer, and area of specialization. Generally, they work in environments that foster research, innovation, and collaboration. Here is a description of the typical workplaces for these professionals:

Research Laboratories: Many computer and information research scientists work in research laboratories, either in academic institutions or private companies. These labs provide a dedicated space for conducting experiments, developing prototypes, and analyzing data. Research laboratories are equipped with advanced computer systems, high-performance servers, specialized software, and cutting-edge research tools to support their work.

Academic Institutions: Research scientists in computer and information science often work in universities or research institutes. They may be affiliated with a particular department or research center within the institution. Academic environments provide access to extensive research resources, such as libraries, research grants, and collaborations with other faculty members and students.

Industrial Research and Development (R&D) Centers: Many large technology companies have dedicated R&D centers where computer and information research scientists work on developing new technologies, software, or hardware products. These centers provide a stimulating and innovative environment with access to state-of-the-art facilities, collaborative teams, and resources for bringing research ideas to practical applications.

Government Research Agencies: Some computer and information research scientists work in government research agencies, such as national laboratories or defense research organizations. These agencies focus on research and development in areas of national interest, including cybersecurity, data analysis, information assurance, and emerging technologies. Government research agencies often collaborate with academia and industry on projects of strategic importance.

Collaboration and Fieldwork: Depending on their research focus, computer and information research scientists may engage in collaborative projects with other researchers, industry partners, or government agencies. This can involve fieldwork, where they collect data or conduct experiments in real-world settings. For example, researchers studying human-computer interaction may conduct user studies in various environments to gather data and evaluate the usability of systems.

Conferences and Workshops: Research scientists often attend conferences, workshops, and seminars relevant to their areas of expertise. These events provide opportunities to present research findings, exchange ideas, and network with other professionals in the field. Presenting research at conferences enables scientists to receive feedback, gain exposure, and stay updated with the latest developments in their areas of research.

Collaboration Tools and Remote Work: With advancements in communication technology, computer and information research scientists may also work remotely or utilize collaboration tools to work with colleagues from different locations. Remote work and virtual collaboration platforms allow for global collaboration, enabling scientists to collaborate with experts from around the world and exchange ideas without physical constraints.

Computer and Information Research Scientists are also known as: Computer Research Scientist

Princeton University

Princeton engineering, grad alum avi wigderson wins turing award for groundbreaking insights in computer science.

By Scott Lyon

April 10, 2024

Avi Wigderson has won the 2023 Turing Award from the Association for Computing Machinery. Photos by Andrea Kane, courtesy of the Institute for Advanced Study

Princeton graduate alumnus Avi Wigderson has won the 2023 A.M. Turing Award from the Association for Computing Machinery (ACM), recognizing his profound contributions to the mathematical underpinnings of computation.

The Turing Award is considered the highest honor in computer science, often called the “Nobel Prize of Computing.”

Wigderson, the Herbert H. Maass Professor in the Institute for Advanced Study ’s School of Mathematics, earned his Ph.D. from Princeton in 1983 in what was then the Department of Electrical Engineering and Computer Science.

In addition to the Turing Award, he is also the recipient of the 2021 Abel Prize , considered the highest honor in mathematics, from the Norwegian Academy of Science and Letters. He is the only person ever to have won both the Abel Prize and the Turing Award.

“Mathematics is foundational to computer science and Wigderson’s work has connected a wide range of mathematical sub-areas to theoretical computer science,” ACM President Yannis Ioannidis said in a statement released by the organization.

“Avi Wigderson is a giant in the field of theoretical computer science, bringing fundamental insights to deep questions about what can — or cannot — be computed efficiently,” said Jennifer Rexford , Princeton’s provost and Gordon Y.S. Wu Professor of Engineering . “He is also a wonderful colleague and a longtime friend of the University.”

Wigderson is best known for his work on computational complexity theory, especially the role of randomness in computation. Namely, in a series of highly influential works from the 1990s, Wigderson and colleagues proved that computation can be efficient without randomness, shaping algorithm design ever since. He has also established important ideas in several other areas, including protocol design and cryptography, which enables much of today’s digital infrastructure.

While his work is primarily mathematical, the notions he is trying to understand through that work are computational, Wigderson said in a video released by the Institute for Advanced Study (IAS). That approach has earned him a reputation as one of the most versatile minds in either discipline.

“He is one of the most central people in theoretical computer science, generally,” said Ran Raz , a professor of computer science at Princeton, who was Wigderson’s graduate student at the Hebrew University in Jerusalem.

Wigderson has influenced countless students and thinkers, having mentored more than 100 postdocs and collaborated with an unusually broad range of scholars. “He is always able to make connections between things,” Raz said.

“He’s an inspiration,” said Pravesh Kothari , an assistant professor of computer science at Princeton and a former postdoctoral advisee of Wigderson’s at IAS. “He’s a role model. If I could become 10 percent of the researcher he is, it would be a fantastic success for my career.” Kothari also said Wigderson implores young researchers to view the entire endeavor as one field. And that approach shows up in all of his work, connecting disparate problems from sub-disciplines that are normally seen as unrelated.

His research has “set the agenda in theoretical computer science” for decades, Google Senior Vice President Jeff Dean said in the ACM press release. His work has also found its way directly into everyday life.

In a series of findings at the intersection of mathematics and computer science, Wigderson cemented what is known as the zero-knowledge proof, critical in cryptography and digital security. The technique has found purchase in modern applications of privacy, compliance, identity verification and blockchain technology.

Raz said he was amazed at how far Wigderson’s ideas had traveled, from the depths of mathematics to the technologies that enable global enterprise to the everyday lives of billions of people. “It’s quite amazing that these things can be made practical,” Raz said.

Szymon Rusinkiewicz , the David M. Siegel ’83 Professor of Computer Science and department chair, added that Wigderson has been a great friend to Princeton’s computer science community, including to students and young scholars. “He has had a great influence throughout the world of computer science, and we especially feel that at Princeton, where he has been a great mentor and collaborator.”

Wigderson is the recipient of numerous other awards, including the 1994 IMU Abacus Medal, the 2009 Gödel Prize and the 2019 Donald E. Knuth Prize. He is currently a Fellow of the ACM, a member of the American Academy of Arts and Sciences and a member of the National Academy of Sciences.

At Princeton, in addition to his Ph.D., he earned an M.S.E. in 1981, an M.A. in 1982, and he later served on Princeton’s computer science faculty from 1990 to 1992. He joined IAS in 1999, where he established the program in Computer Science and Discrete Mathematics.

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The research conducted at google has broadened dramatically, becoming more important to our mission than ever before..

We aim to create a research environment rich in opportunities for product impact, to build a product environment that actively benefits from research, and to provide our staff the freedom to work on important research problems that go beyond immediate product needs.

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Our teams in Atlanta focus on theoretical and application aspects of computer science with a strong focus on machine learning and the algorithmic foundations and theoretical underpinnings of deep learning, with applications to natural language understanding, machine perception, robotics, and ubiquitous computing and sensing.

Our teams in Cambridge work closely with academics at local universities as well as collaborators at local institutes with a goal to impact both Google’s products and general scientific progress. We accomplish this by releasing open source tools, publishing our work and sharing our findings with the academic community.

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Google Research in Montreal performs both open-ended and applied research, in numerous areas including reinforcement learning, meta-learning, optimization, program synthesis, generative modeling, machine translation, and more. We also support the local academic community and have several academic collaborations, including with Mila – Quebec Artificial Intelligence Institute.

Our headquarters has come a long way from its humble roots in a Menlo Park garage, but our innovative Silicon Valley spirit is stronger than ever. On our largest campus, we work on cutting-edge products that are changing the way billions of people use technology. Onsite benefits like fitness and wellness centers embody our philosophy that taking care of Googlers is good for all of us. Build team skills with a group cooking class or coffee tasting, ride a gBike to one of our cafés, or work up a sweat in a group class. Here at the Googleplex, we’re looking for innovators, collaborators, and blue-sky thinkers. We’re looking for you.

We work in close collaboration with academia, with a goal to impact both Google’s products and general scientific progress. We accomplish this in two ways: by releasing software libraries, a way to build research findings into products and services, and through publishing our work and sharing our findings with the academic community.

Our team in Pittsburgh conducts research in natural language processing, machine learning, image and video understanding, and optimization, and our impacts range from academic paper publications to software systems used throughout Google. We collaborate closely with research and applied groups in many areas, and also work closely with Carnegie Mellon University and other organizations in the extremely strong computer science community in Pittsburgh.

As our company headquarters, Mountain View and the surrounding offices in Sunnyvale, San Francisco, and San Bruno are home to many of our world-class research teams and the innovative projects they work on.

Our research teams in Seattle and Kirkland work on a wide range of disciplines — from quantum computing to applied science to federated learning and health. In doing the above, and more, a large focus of our work also focuses on advancing the state of the art in machine learning.

Nestled between the Santa Cruz Mountains and the San Francisco Bay, with San Jose to the south, San Francisco to the north, and NASA right next door, you’ll find one of Google’s largest and newest global campuses in Sunnyvale. Here in the heart of the original Silicon Valley innovation is happening everywhere—from our Cloud team developing exciting new products and services, to moving into our latest office spaces which include interconnected building projects, the creation of green spaces connecting campuses with the community, and the creative restoration of local habitats. We love growing in Sunnyvale—and you will too.

We develop novel neural network architectures and learning algorithms, with applications to computer vision, natural language and speech processing, medical image analysis, and computer architecture and software.

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Google Research teams in Accra collaborate with global research teams to lead many sustainability initiatives of particular interest to Africa. We implement theoretical and applied artificial intelligence with a strong focus on machine learning and algorithmic foundations to tackle some global challenges, such as food security, disaster management, remote sensing, among others.

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In Berlin, our teams work on a range of topics from foundational to more applied and involve data comprised of text, images, video, audio and more. We are engaging and collaborating closely with Berlin’s vibrant academic and startup communities.

We work on machine learning, natural language understanding and machine perception, from foundational research to AI innovations, in search, healthcare, and crisis response.

We work on natural language understanding and conversational dialog, text-to-speech, (on-device) machine learning, human-centered AI research and user research as well as healthcare.

We work on problems in quantum computing as well as speech and language processing, and collaborate closely with Google’s product teams across the world.

We tackle big challenges across several fields at the intersection of computer science, statistics and applied mathematics while collaborating closely with a strong academic community.

We solve big challenges in computer science, with a focus on machine learning, natural language understanding, machine perception, algorithms and data compression.

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Google Research Australia aims to advance the state-of-the-art in machine learning, in areas such as Fundamental Machine Learning, Natural Language Understanding, and Systems Programming. We aim to apply our research in ways that benefit Australia, Google and global society.

We are interested in advancing the state of the art and applications in areas like Machine Learning, Natural Language Understanding, Computer Vision, Software Engineering and Multi-agent Systems.

We are interested in advancing the state of the art and applications in areas like machine learning, speech, and natural language processing.

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Internships take place throughout the year, and we encourage students from a range of disciplines, including CS, Electrical Engineering, Mathematics, and Physics to apply to work with us.

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We’re proud to work with academic and research institutions that push the boundaries of AI and computer science.

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Computer Science, BA (Hons) and MEng

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Computer Science at Cambridge

Computer Science at Cambridge brings together disciplines including mathematics, engineering, the natural sciences, psychology and linguistics.

Study modern computer science, along with the underlying theory and foundations in economics, law and business.  

Here at Cambridge, we pioneered computer science and we continue to lead its development today.

Our links to Computing go back to the 1930s when Alan Turing developed the theoretical foundations for computation. We’ve been at the forefront of Computer Science research ever since.

This is a broad and deep course that covers all aspects of modern computer science.

We have 3 and 4 year course options:

• the 3-year course is a BA honours degree
• the 4-year course includes a Masters, leading to a BA and Master of Engineering (MEng) degree

Whichever option you choose, you will develop practical skills in:

• programming, in various languages such as OCaml, Java, C/C++ and Prolog
• hardware systems, such as chip design

Teaching and facilities

We are the oldest Computer Science department in the world – our Computer Lab has been at the forefront of research in Computer Science ever since its inception, in 1970.

We offer a learning environment that is creative, stimulating, modern and entrepreneurial. You will be taught by pioneers and leading researchers in this fast-moving field.

You'll also take part in group projects which will be presented to external companies. Find out more about how Computer Science at Cambridge can support your future career.

The Department of Computer Science and Technology is packed with the latest technology. Our facilities include:

• advanced lecture theatres
• dedicated practical rooms

Our West Cambridge site, home of the Computer Laboratory, offers:

• a fantastic environment for both study and relaxation
• a large library stocked with the latest Computer Science publications
• big and comfortable lecture theatres
• a great café

At Cambridge, you'll also have access to the impressive Cambridge University Library, one of the world’s oldest university libraries.

Course costs

When you go to university, you’ll need to consider two main costs – your tuition fees and your living costs (sometimes referred to as maintenance costs).

Your living costs will include costs related to your studies that are not covered by your tuition fees. There are some general study costs that will apply for all students – you can find details of these costs here .

Other additional costs for Computer Science are detailed below. If you have any queries about resources/materials, please contact the Department.

• Laptop specification: £800 for a modern entry-level laptop is sufficient, but we recommend at least half the main drive is dedicated to a bootable Linux system, such as Ubuntu.
• University approved scientific calculator: please see the Department website for details.

You don't have to buy your own copies of textbooks, but it's strongly recommended. The number of textbooks you need depends on the course options you’ve chosen. The costs below are an estimate of how much you can expect to spend each year if you do purchase your own copies.

• Year 1: Estimated cost of core texts £150.
• Years 2, 3 and 4: Estimated cost of core texts £150 to £250 per year.

Your future career

There are more than 1,000 specialist computing and advanced technology companies and commercial laboratories in the Cambridge area, known as ‘Silicon Fen'.

A number of local firms and start-ups support our teaching and employ our graduates, in areas from chip design to mathematical modelling and AI.

As a graduate, you’ll have knowledge and skills that embody principles which will outlast today’s technology. This makes you highly sought after by industry and commerce alike.

Many of our graduates go on to work as:

• programmers
• software development professionals

Other graduates decide to pursue:

• further study
• careers in teaching and research

Many have also founded companies, or gained employment in:

• the games industry
• communications

Teaching is provided through lectures, practical classes and small-group supervisions.

In your first year you will typically have 20 hours of teaching each week, including up to 12 lectures and practical classes.

In your first and second year you will be assessed through 3-hour examinations, taken in the final term of each year.

In your third year you will be assessed through coursework and 3-hour examinations.

Practical work is undertaken and assessed in all years of the degree programme.

You won't usually be able to resit any of your exams.

Year 1 (Part IA)

You take 4 papers, including 3 compulsory Computer Science papers, covering topics such as:

• foundations of computer science, taught in OCaml
• Java and object-oriented programming
• operating systems
• digital electronics
• interaction design
• machine learning

You will also take a Mathematics paper, from the first year of the Natural Sciences course.

Year 2 (Part IB)

You take 4 papers, spanning core topics:

• theory – including logic and proof, computation theory
• systems – including computer architecture, computer networking
• programming – including compiler construction, programming in C/C++
• human aspects – including Human Interaction design, Artificial Intelligence

You also undertake a group project, which reflects current industrial practice.

Year 3 (Part II)

You choose from a large selection of topics which allows you to concentrate on an area of interest to you, such as:

• computer architecture
• applications (including bioinformatics and natural language processing)

New topics inspired by current research interests include computer architecture, data science and robotics.

You will also work on a substantial project that demonstrates your computer science skills, and write a 10,000 to 12,000 word dissertation on it.

Projects are often connected with current Cambridge research, and many utilise cutting-edge technology.

Year 4 (Part III, optional Masters)

The fourth year is designed for students considering a career in academic or industrial research.

• explore issues at the very forefront of computer science
• undertake a substantial research project

Progression to fourth year depends on how well you do in your third year exams.

If you successfully complete the fourth year, you’ll get the MEng qualification, as well as the BA degree which you get at the end of the third year.

• For further information about this course and the papers you can take see the Faculty of Computer Science and Technology website .

Changing course

It’s really important to think carefully about which course you want to study before you apply. 

In rare cases, it may be possible to change course once you’ve joined the University. You will usually have to get agreement from your College and the relevant departments. It’s not guaranteed that your course change will be approved.

You might also have to:

• take part in an interview
• complete an admissions test
• produce some written work
• achieve a particular grade in your current studies
• do some catch-up work
• start your new course from the beginning 

For more information visit the Faculty website .

You can also apply to change to:

• Management Studies at the Judge Business School

You can't apply to this course until you're at Cambridge. You would usually apply when you have completed 1 year or more of your original Cambridge course.

You should contact your College’s Admissions Office if you’re thinking of changing your course. They will be able to give you advice and explain how changing courses works.

Minimum offer level

A level: A*A*A IB: 41-42 points, with 776 at Higher Level Other qualifications : Check which other qualifications we accept .

Subject requirements

To apply to any of our Colleges for Computer Science, you will need A levels/IB Higher Levels (or the equivalent) in: 

• Mathematics 
• Further Mathematics to AS or A level if your school offers it. Please see the further guidance below. 

If you’re studying IB, we ask for Analysis and Approaches for this course. If this isn’t an option at your school, please contact the College you wish to apply to for advice. 

If you’re applying to Churchill, Downing or Lucy Cavendish, you will also need a third science subject at A level/IB Higher Level. If you apply to Christ's College you must have Further Mathematics A level.

Colleges will require A*/7 in Mathematics or Further Mathematics. Colleges may also require an A*/7 in specific subjects as part of your offer. If you apply to Churchill College they require an A*/7 in Chemistry, Computer Science or Physics as well as Mathematics or Further Mathematics. 

These subject requirements are provisional for 2025 entry. Please check back in April 2024 for confirmed details.

Further Mathematics A level and additional maths 

If your school offers Further Mathematics to AS or A level, you should take it.  

Additional mathematics is helpful and all candidates are strongly encouraged to take up opportunities to develop their skills, such as by participating in olympiads or accessing the online resources in the Advanced Mathematics Support Programme .

What Computer Science students have studied

Most Computer Science students (who had studied A levels and started at Cambridge in 2017-19) achieved at least A*A*A* (81% of entrants).

All of these students studied Mathematics and most also took:

• Further Mathematics (96%)
• Physics (85%)
• Computing (59%)

The majority of students who studied IB achieved at least 43 points overall.

Check our advice on choosing your high school subjects . You should also check if there are any required subjects for your course when you apply.

Admission assessment

All applicants for Computer Science for 2025 entry are required to take the Test of Mathematics for University Admission (TMUA) at an authorised assessment centre. You must register in advance for this test.

Please see the admissions test page for more information.

Check the TMUA page for further details and example papers.

Submitted work

You won't usually be asked to submit examples of written work. You may be asked to do some reading prior to your interview, but if this is required the College will provide full details in your interview invitation.

Offers above the minimum requirement

The minimum offer level and subject requirements outline the minimum you'll usually need to achieve to get an offer from Cambridge.

In some cases, you'll get a higher or more challenging offer. Colleges set higher offer requirements for a range of reasons. If you'd like to find out more about why we do this,  check the information about offers above the minimum requirement  on the entry requirements page.

Some Colleges usually make offers above the minimum offer level. Find out more on our qualifications page .

All undergraduate admissions decisions are the responsibility of the Cambridge Colleges. Please contact the relevant  College admissions office  if you have any queries.

Discover your department or faculty

• Visit the Department of Computer Science and Technology website - The Department of Computer Science and Technology website has more information about this course, facilities, people and research.

Explore our Colleges

• Find out how Colleges work - A College is where you’ll live, eat and socialise. It’s also where you’ll have teaching in a small group, known as supervisions.
• How to choose a Cambridge College that's right for you - If you think you know which course you’d like to study, it’s time to choose a College.

Visit us on open day

• Book an open day - Get a feel for the city and the University.
• Find an event - We offer a range of events where you can find out more about Cambridge, Colleges, and your course. Many of our events have hybrid options so you can join us virtually.

Find out how to apply

• Find out how to apply and how our admissions processes work - Our admissions process is slightly different to other universities. We’ve put together a handy guide to tell you everything you need to know about applying to study at Cambridge.
• Improve your application - Supercurricular activities are a great way to engage with your chosen subject outside of school or college.

Discover Uni data

Contextual information.

Discover Uni allows you to compare information about individual courses at different higher education institutions.  This can be a useful method of considering your options and what course may suit you best.

However, please note that superficially similar courses often have very different structures and objectives, and that the teaching, support and learning environment that best suits you can only be determined by identifying your own interests, needs, expectations and goals, and comparing them with detailed institution- and course-specific information.

We recommend that you look thoroughly at the course and University information contained on these webpages and consider coming to visit us on an Open Day , rather than relying solely on statistical comparison.

You may find the following notes helpful when considering information presented by Discover Uni.

• Discover Uni relies on superficially similar courses being coded in the same way. Whilst this works on one level, it may lead to some anomalies. For example, Music courses and Music Technology courses can have exactly the same code despite being very different programmes with quite distinct educational and career outcomes. Any course which combines several disciplines (as many courses at Cambridge do) tends to be compared nationally with courses in just one of those disciplines, and in such cases the Discover Uni comparison may not be an accurate or fair reflection of the reality of either. For example, you may find that when considering a degree which embraces a range of disciplines such as biology, physics, chemistry and geology (for instance, Natural Sciences at Cambridge), the comparison provided is with courses at other institutions that primarily focus on just one (or a smaller combination) of those subjects.You may therefore find that not all elements of the Cambridge degree are represented in the Discover Uni data.
• Some contextual data linked from other surveys, such as the National Student Survey (NSS) or the Destination of Leavers in Higher Education (DLHE), may not be available or may be aggregated across several courses or several years due to small sample sizes.  When using the data to inform your course choice, it is important to ensure you understand how it has been processed prior to its presentation. Discover Uni offers some explanatory information about how the contextual data is collated, and how it may be used, which you can view here: https://discoveruni.gov.uk/about-our-data/ .
• Discover Uni draws on national data to provide average salaries and employment/continuation data.  Whilst starting salaries can be a useful measure, they do not give any sense of career trajectory or take account of the voluntary/low paid work that many graduates undertake initially in order to gain valuable experience necessary/advantageous for later career progression. Discover Uni is currently piloting use of the Longitudinal Education Outcomes (LEO) data to demonstrate possible career progression; it is important to note that this is experimental and its use may be modified as it embeds.

The above list is not exhaustive and there may be other important factors that are relevant to the choices that you are making, but we hope that this will be a useful starting point to help you delve deeper than the face value of the Discover Uni data.

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Office of the Vice President for Research

Four clas faculty researchers secure prestigious early career awards.

Continuing  an upward trend of University of Iowa faculty securing prestigious early-career grants, four investigators from the Departments of Physics and Astronomy and Computer Science have been awarded notable grant awards to advance their careers.

DeRoo, Hoadley advance space instrumentation with Nancy Grace Roman Technology Fellowships in Astrophysics for Early Career Researchers

Casey DeRoo and Keri Hoadley , both assistant professors in the Department of Physics and Astronomy, each received a Nancy Grace Roman Technology Fellowship in Astrophysics for Early Career Researchers. The NASA fellowship provides each researcher with $500,000 over two years to support their research in space-based instrumentation. 

Hoadley’s research is two-pronged. She will design and ultimately prototype a mirror-based vacuum ultraviolet polarizer, which will allow researchers to access polarized light from space below 120-nanometer wavelength. Polarizing light at such a low wavelength is crucial to building optics for NASA’s future Habitable World Observatory (HWO), the agency’s next flagship astrophysics mission after the Nancy Grace Roman Space Telescope. 

“Our vacuum ultraviolet polarizer project is meant to help set up our lab to propose to NASA for one or more follow-up technology programs, including adapting this polarizer for use in vacuum systems, duplicating it and measuring its efficiency to measure additional flavors of polarized UV light, quantifying the polarization effects introduced by UV optical components that may be used on HWO, and building an astronomical instrument to measure the polarization of UV from around massive stars and throughout star-forming regions,” said Hoadley.

In addition, Hoadley and her team will build a facility to align, calibrate, and integrate small space telescopes before flight, using a vacuum chamber and wavelengths of light typically only accessible in space, which could help the university win future small satellite and suborbital missions from NASA. 

DeRoo will work to advance diffraction gratings made with electron beams that pattern structures on a nanometer scale.   Like a prism, diffraction gratings spread out and direct light coming from stars and galaxies, allowing researchers to deduce things like the temperature, density, or composition of an astronomical object.

The fellowship will allow DeRoo to upgrade the university’s Raith

 Voyager tool, a specialized fabrication tool hosted by OVPR’s Materials Analysis, Testing and Fabrication (MATFab) facility.

“These upgrades will let us perform algorithmic patterning, which uses computer code to quickly generate the patterns to be manufactured,” DeRoo said. “This is a major innovation that should enable us to make more complex grating shapes as well as make gratings more quickly.” DeRoo added that the enhancements mean his team may be able to make diffraction gratings that allow space instrument designs that are distinctly different from those launched to date.

“For faculty who develop space-based instruments, the Nancy Grace Roman Technology Fellowship is on par with the prestige of an NSF CAREER or Department of Energy Early Career award,” said Mary Hall Reno, professor and department chair. “Our track record with the program elevates our status as a destination university for astrophysics and space physics missions.”

Uppu pursues building blocks quantum computing with NSF CAREER Award

Ravitej Uppu, assistant professor in the Department of Physics and Astronomy, received a 5-year NSF CAREER award of $550,000 to conduct research aimed at amplifying the power of quantum computing and making its application more practical. 

Uppu and his team will explore the properties of light-matter interactions at the level of a single photon interacting with a single molecule, enabling them to generate efficient and high-quality multiphoton entangled states of light. Multiphoton entangled states, in which photons become inextricably linked, are necessary for photons to serve as practical quantum interconnects, transmitting information between quantum computing units, akin to classical cluster computers. 

“ In our pursuit of secure communication, exploiting quantum properties of light is the final frontier,” said Uppu. “However, unavoidable losses that occur in optical fiber links between users can easily nullify the secure link. Our research on multiphoton entangled states is a key building block for implementing ‘quantum repeaters’ that can overcome this challenge.”

Jiang tackles real-world data issues with NSF CAREER Award

Peng Jiang, assistant professor in the Department of Computer Science, received an NSF CAREER Award that will provide $548,944 over five years to develop tools to support the use of sampling-based algorithms. 

Sampling-based algorithms reduce computing costs by processing only a random selection of a dataset, which has made them increasingly popular, but the method still faces limited efficiency. Jiang will develop a suite of tools that simplify the implementation of sampling-based algorithms and improve their efficacy across wide range of computing and big data applications.

“ A simple example of a real-world application is subgraph matching,” Jiang said. “For example, one might be interested in finding a group of people with certain connections in a social network. The use of sampling-based algorithms can significantly accelerate this process.”

In addition to providing undergraduate students the opportunity to engage with this research, Jiang also plans for the project to enhance projects in computer science courses.

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Search form, resources for, oberlin’s tanisha shende named national student employee of the year.

April 12, 2024

Communications Staff

Tanisha Shende, a second-year student majoring in computer science and mathematics, has been named National Student Employee of the Year and Student Employee of the Year for Diversity and Inclusion, twin honors conferred by the National Student Employment Association (NSEA) this week.

The accolades were made possible through the effort of the Student Employment Office , which coordinated the nomination process.

Originally from Lodi, New Jersey, Shende is a key supporter of STEM studies at Oberlin and a driving force in the effort to ease the acclimation to college among first-generation and other underrepresented students. In the Office of Undergraduate Research , she serves as chair of the collective advocacy committee for Bridging Resources and Access to Nurture Community through Holistic Engagement in STEM (BRANCHES).

Shende is an active member of the STRONG (Science and Technology Research Opportunities for a New Generation) program and played a pivotal role in the successful merger of two other Oberlin programs—Roots in STEM, an identity-based residence hall cluster, and the Center for Learning, Education, and Research in the Sciences, or CLEAR —and improve student support in the process.

In her own research, Shende is part of a study examining classrooms and research spaces to identify barriers to STEM learning. She is also a member of a team working to make virtual and augmented reality technology more accessible to people with sensory processing disorders.

“Tanisha is one of those unique students who has a natural spark,” says Zach Slimak , Oberlin’s STEM program coordinator, who nominated Shende for the NSEA honors. “She recognizes how hard it can be for a student to find a community and a sense of belonging. She cares deeply about her position and goes above and beyond every day. She’s always willing to learn, always willing to teach, and always willing to be a leader for change. I am honored to know Tanisha and be a small part of her commitment to diversity, equity, and inclusion.”

Shende learned of the honors at an April 9 gathering on campus that—to her surprise—was held in her honor. The guest list included Oberlin President Carmen Twillie Ambar, Vice President and Dean of Students Karen Goff , and Shende’s staff colleagues from the Office of Undergraduate Research—part of the Center for Engaged Liberal Arts , or CELA.

The honors coincide with Oberlin’s celebration of National Student Employment Week.

“I’m so grateful to everyone involved for making the reception such a special moment,” Shende says. “It’s an honor to be recognized for my efforts and be surrounded by incredible supporters. I’m looking forward to continuing my work!”

The NSEA is dedicated to promoting professional development, recognition, and advocacy for student employees in higher education. Oberlin joined the organization in 2022; this year marked the first time it has submitted a nominee for Student Employee of the Year. The NSEA confers awards in five categories: Community Service, Diversity and Inclusion, Leadership, Technology and Innovation, and Critical Thinking, as well as the overall award for Student Employee of the Year.

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Can large language models replace human participants in some future market research?

by Institute for Operations Research and the Management Sciences

Do market researchers still need to conduct original research using human participants in their work? Not always, according to a new study. The study found that thanks to the increasing sophistication of large language models (LLMs), human participants can be substituted with LLMs and still generate similar outputs as those generated from human surveys.

The study is published in Marketing Science . The article, "Determining the Validity of Large Language Models for Automated Perceptual Analysis," is authored by Peiyao Li and Zsolt Katona, both of the University of California, Berkeley; Noah Castelo of the University of Alberta and Miklos Sarvary of Columbia University.

According to the research, agreement rates between human- and LLM-generated data sets reached 75%–85%.

"LLMs can be used to generate text when given a prompt on certain generative Artificial Intelligence (AI) platforms," says Li. "Our research focused on perceptual analysis and the use of automated market research for certain product categories."

To conduct their research, the study authors used LLMs to tap data that is broadly available on the internet. They developed a new methodology and workflow that allows market researchers to rely only on an LLM to conduct market research. As a result, they demonstrated that LLM-powered market research can produce meaningful results and even replicate human results.

"It is important to note that with LLMs, while market researchers may not require interviews with human research subjects, the ultimate data does originate from human beings, using available data," says Katona. "LLMs have been engineered to accurately replicate human responses based on machine learning of actual human perceptions, attitudes, and preferences."

Castelo added, "The core LLM takes a prompt as an input and generates a continuation of text as output. With proper prompting, the LLM can then generate comparisons and assessments of various brands or products in a given category and produce results that are, at the moment, 75%–85% in agreement with research featuring human participants ."

The researchers believe that for some product and brand categories, their new method of fully or partially automating market research will increase the efficiency of market research by speeding up the process and potentially reducing cost. At the same time, they caution that fully automated market research without human input may not be accurate for all product categories.

"While we are very excited about the possibilities we've seen through our research, we recognize that this is just the beginning and going forward, LLM-based market research will be able to answer more nuanced questions as the market research field begins to tap and develop its potential," says Sarvary.

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