computer research

• Values of Inclusion
• 2020 Antiracism Task Force
• 2022 DEI Report
• Research News
• Department Life
• Listed by Recipient
• Listed by Category
• Oral History of Cornell CS
• CS 40th Anniversary Booklet
• ABC Book for Computer Science at Cornell by David Gries
• Books by Author
• Books Chronologically
• The 60's
• The 70's
• The 80's
• The 90's
• The 00's
• The 2010's
• Faculty Positions: Ithaca
• Faculty Positions: New York City
• Lecturer Position: Ithaca
• Post-doc Position: Ithaca
• Staff/Technical Positions
• Ugrad Course Staff
• Ithaca Info
• Internal info
• Graduation Information
• Cornell Learning Machines Seminar
• Student Colloquium
• Fall 2024 Colloquium
• Conway-Walker Lecture Series
• Salton 2024 Lecture Series
• Spring 2024 Artificial Intelligence Seminar
• Spring 2024 Robotics Seminar
• Spring 2024 Theory Seminar
• Big Red Hacks
• Cornell University - High School Programming Contests 2024
• Game Design Initiative
• CSMore: The Rising Sophomore Summer Program in Computer Science
• Explore CS Research
• ACSU Research Night
• Cornell Junior Theorists' Workshop 2023
• Researchers
• Ph.D. Students
• M.Eng. Students
• M.S. Students
• Ph.D. Alumni
• M.S. Alumni
• List of Courses
• Course and Room Roster
• CS Advanced Standing Exam
• Architecture

Artificial Intelligence

Computational biology, database systems, human interaction, machine learning, natural language processing, programming languages, scientific computing, software engineering, systems and networking, theory of computing.

• Contact Academic Advisor
• Your First CS Course
• Technical Electives
• CS with Other Majors/Areas
• Transfer Credits
• CS Honors Program
• CPT for International CS Undergrads
• Graduation Requirements
• Useful Forms
• Becoming a CS Major
• Requirements
• Game Design Minor
• Co-op Program
• Cornell Bowers CIS Undergraduate Research Experience (BURE)
• Independent Research (CS 4999)
• Student Groups
• UGrad Events
• Undergraduate Learning Center
• UGrad Course Staff Info
• The Review Process
• Early M.Eng Credit Approval
• Financial Aid
• Prerequisites
• The Application Process
• The Project
• Pre-approved Electives
• Degree Requirements
• The Course Enrollment Process
• Advising Tips
• Entrepreneurship
• Cornell Tech Programs
• Professional Development
• Contact MEng Office
• Career Success
• Applicant FAQ
• Computer Science Graduate Office Hours
• Exam Scheduling Guidelines
• Graduate TA Handbook
• MS Degree Checklist
• MS Student Financial Support
• Special Committee Selection
• Diversity and Inclusion
• Contact MS Office
• Ph.D. Applicant FAQ
• Graduate Housing
• Non-Degree Application Guidelines
• Ph. D. Visit Day
• Advising Guide for Research Students
• Business Card Policy
• Cornell Tech
• Curricular Practical Training
• A & B Exam Scheduling Guidelines
• Fellowship Opportunities
• Field of Computer Science Ph.D. Student Handbook
• Field A Exam Summary Form
• Graduate School Forms
• Instructor / TA Application
• Ph.D. Requirements
• Ph.D. Student Financial Support
• Travel Funding Opportunities
• Travel Reimbursement Guide
• The Outside Minor Requirement
• CS Graduate Minor
• Outreach Opportunities
• Parental Accommodation Policy
• Special Masters
• Student Spotlights
• Contact PhD Office

Search form

You are here

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.

Research Areas

Knowledge representation, machine learning, NLP and IR, reasoning, robotics, search, vision

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

Computer Architecture & VLSI

Processor architecture, networking, asynchronous VLSI, distributed computing

Database systems, data-driven games, learning for database systems, voice interfaces, computational fact checking, data mining

Interactive rendering, global illumination, measurement, simulation, sound, perception

HCI, interface design, computational social science, education, computing and society

Artificial intelligence, algorithms

Programming language design and implementation, optimizing compilers, type theory, formal verification

Perception, control, learning, aerial robots, bio-inspired robots, household robots

Numerical analysis, computational geometry, physically based animation

Secure systems, secure network services, language-based security, mobile code, privacy, policies, verifiable systems

The software engineering group at Cornell is interested in all aspects of research for helping developers produce high quality software.

Operating systems, distributed computing, networking, and security

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

Computer vision

Grab your spot at the free arXiv Accessibility Forum

Help | Advanced Search

Computer Science (since January 1993)

For a specific paper , enter the identifier into the top right search box.

• new (most recent mailing, with abstracts)
• recent (last 5 mailings)
• current month's listings
• specific year/month: 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 all months 01 (Jan) 02 (Feb) 03 (Mar) 04 (Apr) 05 (May) 06 (Jun) 07 (Jul) 08 (Aug) 09 (Sep) 10 (Oct) 11 (Nov) 12 (Dec)
• Catch-up: Changes since: 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 01 (Jan) 02 (Feb) 03 (Mar) 04 (Apr) 05 (May) 06 (Jun) 07 (Jul) 08 (Aug) 09 (Sep) 10 (Oct) 11 (Nov) 12 (Dec) 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 , view results without with abstracts
• Search within the cs archive
• Article statistics by year: 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993

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.AR - Hardware Architecture ( new , recent , current month ) Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
• 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.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.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.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.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.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.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.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.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.
• 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.HC - Human-Computer Interaction ( new , recent , current month ) Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
• cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
• cs.IT - Information Theory ( new , recent , current month ) Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
• 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.
• 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.MA - Multiagent Systems ( new , recent , current month ) Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
• cs.MM - Multimedia ( new , recent , current month ) Roughly includes material in ACM Subject Class H.5.1.
• cs.MS - Mathematical Software ( new , recent , current month ) Roughly includes material in ACM Subject Class G.4.
• cs.NA - Numerical Analysis ( new , recent , current month ) cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
• 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.
• 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.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
• 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.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.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
• 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.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.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.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.

• Who’s Teaching What
• Subject Updates
• MEng program
• Opportunities
• Minor in Computer Science
• Resources for Current Students
• Program objectives and accreditation
• Graduate program requirements
• Admission process
• Degree programs
• Graduate research
• EECS Graduate Funding
• Resources for current students
• Student profiles
• Instructors
• DEI data and documents
• Recruitment and outreach
• Community and resources
• Get involved / self-education
• Rising Stars in EECS
• Graduate Application Assistance Program (GAAP)
• MIT Summer Research Program (MSRP)
• Sloan-MIT University Center for Exemplary Mentoring (UCEM)
• Electrical Engineering
• Computer Science
• Artificial Intelligence + Decision-making
• AI and Society

AI for Healthcare and Life Sciences

Artificial intelligence and machine learning.

• Biological and Medical Devices and Systems

Communications Systems

• Computational Biology

Computational Fabrication and Manufacturing

Computer architecture, educational technology.

• Electronic, Magnetic, Optical and Quantum Materials and Devices

Graphics and Vision

Human-computer interaction.

• Information Science and Systems
• Integrated Circuits and Systems
• Nanoscale Materials, Devices, and Systems
• Natural Language and Speech Processing
• Optics + Photonics
• Optimization and Game Theory

Programming Languages and Software Engineering

Quantum computing, communication, and sensing, security and cryptography.

• Signal Processing

Systems and Networking

• Systems Theory, Control, and Autonomy

Theory of Computation

• Departmental History
• Departmental Organization
• Visiting Committee
• 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

Study: when allocating scarce resources with ai, randomization can improve fairness.

Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.

Study across multiple brain regions discerns Alzheimer’s vulnerability and resilience factors

Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.

The Department of EECS announces new Career Development chairs

The career development chair recipients are Cheng-Zhi Anna Huang, Kuikui Liu, Marzyeh Ghassemi, Kaiming He, and Alexander Rives.

Department of EECS names new chair recipients

The new chairs became effective July 1.

Large language models don’t behave like people, even though we may expect them to

A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.

Upcoming events

Doctoral thesis: approximation and system identification techniques for stochastic biomolecular systems, doctoral thesis: parameterizations of neural fields, doctoral thesis: geometric learning for manipulating scenes and objects, doctoral thesis: programmable interactions between optical fields and atom-like systems in integrated circuits, doctoral thesis: automated and provable privatization for black-box processing, doctoral thesis: representation learning for control: lessons from partially observable linear dynamical systems.

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

Research & Impact

Making an Impact for a Better World

As computing continues to transform our world, the research we're pursuing at Stanford Computer Science seeks to ethically create, shape, and empower the new frontier. From the latest in robotics to foundation models to cryptocurrency, Stanford computer scientists are making an impact on the world beyond our academic walls. 

Faculty Spotlight:  Omer Reingold, the Rajeev Motwani Professor in Computer Science

"A computer scientist teaching a theater class is a bit unusual, I’ll grant you that. But is it so strange? For me, classifying different parts of campus to left-brain-versus-right-brain kind of thinking is just an unfortunate stereotype. I'd much rather go with ‘creativity is creativity is creativity.'" Read Omer Reingold's Story  

In the News: See Our Research in Action

Best Paper Award: "Breaking the Metric Voting Distortion Barrier"

Stanford professor, Moses Charikar, and his two co-authors, Kangning Wang (postdoc) and Prasanna Ramakrishnan (PhD student), win Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA24).

Click here to read more as Kangning and Prasanna discuss their passion for research, the challenges they faced, and the significance of this award.

A Robotic Diver Connects Human's Sight and Touch to the Deep Sea

The Future of AI Chat: Foundation Models and Responsible Innovation

Guest Percy Liang is an authority on AI who says that we are undergoing a paradigm shift in AI powered by foundation models, which are general-purpose models trained at immense scale, such as ChatGPT.

CS Faculty & Their Research

Explore our network of faculty members and the innovation conceived by their research. They are shaping a new era of solutions and the next generation of thought leaders and entrepreneurs.

Meet Our Faculty & Their Research

Stanford Computer Science faculty members work on the world's most pressing problems, in conjunction with other leaders across multiple fields. Fueled by academic and industry cross-collaborations, they form a network and culture of innovation.

The Emmy Award-winning video looks back at a remarkable six decades of AI work at Stanford University.

Stanford has been a leader in AI almost since the day the term was dreamed up by John McCarthy in the 1950s. McCarthy would join the Stanford faculty in 1962 and found the Stanford Artificial Intelligence Lab (SAIL), initiating a six-decades-plus legacy of innovation. Over the years, the field has grown to welcome a diversity of researchers and areas of exploration, including robotics, autonomous vehicles, medical diagnostics, natural language processing, and more. All the while, Stanford has been at the forefront in research and in educating the next generation of innovators in AI. Artificial intelligence would not be what it is today without Stanford.  

Research at the Affiliate Programs

Stanford Computer Science has a legacy of working with industry to advance real-world solutions. Membership in our affiliate programs provides companies with access to the research, faculty, and students to accelerate their innovations.

Join the Affiliates Programs

Interested in the benefits of memberships to our affiliate programs, sponsored research, executive education programs, or student recruitment? Get started by contacting:

Joseph Huang, PhD | Executive Director of Strategic Research Initiatives Stanford University, Computer Science [email protected]  

Connecting Students & Research: Jump In

At Stanford, students do amazing research. Their projects are widely recognized as some of the best in the world. Stanford's reputation as one of the top CS programs comes in large part from this. If you're a student with a passion for participating in meaningful research, our CURIS and LINXS programs are designed to get you started.

LINXS Program

The Stanford LINXS Program is an eight-week summer residential program that brings innovative undergraduates, who are currently attending Historically Black Colleges & Universities and Hispanic Serving Institutions, to Stanford for an immersive academic research and graduate school preparation experience. 

CURIS Program

CURIS is the undergraduate research program of Stanford's Computer Science Department. Each summer, 100+ undergraduates conduct and participate in computer science research advised and mentored by faculty and PhD students.  

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Sustainability
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

Computer science and technology

Download RSS feed: News Articles / In the Media / Audio

New transistor’s superlative properties could have broad electronics applications

Ultrathin material whose properties “already meet or exceed industry standards” enables superfast switching, extreme durability.

July 26, 2024

Read full story →

Study: When allocating scarce resources with AI, randomization can improve fairness

Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.

July 24, 2024

MIT researchers advance automated interpretability in AI models

MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.

July 23, 2024

Large language models don’t behave like people, even though we may expect them to

A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.

AI model identifies certain breast tumor stages likely to progress to invasive cancer

The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.

July 22, 2024

License plates of MIT

Custom plates display expressions of scholarship, creativity, and MIT pride among Institute affiliates.

Creating and verifying stable AI-controlled systems in a rigorous and flexible way

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

July 17, 2024

AI method radically speeds predictions of materials’ thermal properties

The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.

July 16, 2024

How to assess a general-purpose AI model’s reliability before it’s deployed

A new technique enables users to compare several large models and choose the one that works best for their task.

Marking a milestone: Dedication ceremony celebrates the new MIT Schwarzman College of Computing building

Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.

July 12, 2024

Reasoning skills of large language models are often overestimated

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.

July 11, 2024

When to trust an AI model

More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.

“They can see themselves shaping the world they live in”

Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.

July 8, 2024

MIT researchers introduce generative AI for databases

This new tool offers an easier way for people to analyze complex tabular data.

Wireless receiver blocks interference for better mobile device performance

This novel circuit architecture cancels out unwanted signals at the earliest opportunity.

June 27, 2024

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram

• Menu  Close 
• Search 

Bridging disciplines and accelerating discoveries in computer science.

Computer science research at the Johns Hopkins University is advancing computing technology, enabling new modes of thought, and transforming society. Our faculty conduct innovative, collaborative research aimed at solving large and complex interdisciplinary problems, drawing upon the university’s renowned strengths in areas including artificial intelligence, robotics, speech and language processing, medicine, and public health.

The department is rapidly growing, with current core research areas of theory and programming languages; systems and networking; computational biology and medicine; information security; natural language processing; machine learning, artificial intelligence, and data science; robotics, computer vision, and graphics; and human-computer interaction.

Researchers partners with colleagues in other engineering disciplines, as well as with investigators from the Johns Hopkins Krieger School of Arts and Sciences, the School of Medicine, and the Applied Physics Laboratory.

Cross-Departmental Centers and Institutes

• Center for Language and Speech Processing
• Laboratory for Computational Sensing and Robotics
• Johns Hopkins Information Security Institute
• Institute for Data Intensive Engineering and Science
• Malone Center for Engineering in Healthcare
• Human Language Technology Center of Excellence
• Center for Computational Biology
• Mathematical Institute for Data Science

Institute for Assured Autonomy

• Johns Hopkins Data Science and AI Institute

Research Areas

Theory & programming languages.

Focusing on the design, implementation, and use of computer programming languages.

Systems & Networkings

Our faculty are undertaking research into all aspects of computer systems and networks.

Computational Biology & Medicine

Faculty are engaged in a wide range of computational health and biology projects, from using data-driven tools to detect early signs of sepsis to DNA sequencing technology and evolutionary genomics.

Information Security

Hopkins researchers are working to safeguard our digital world.

Natural Language Processing

Creating innovative new technologies that will enable more natural interaction between human and computers.

Machine Learning, AI, & Data Science

Applying cutting-edge machine learning techniques to new datasets and domains.

Robotics, Vision, & Graphics

Research spans the areas of computer vision, computer graphics, and augmented and virtual reality.

Human-Computer Interaction

Placing people at the center of technological innovation.

Computer-Assisted Medicine

Our faculty are shaping the digital future across all aspects of health care.

The Institute for Assured Autonomy is operating in partnership with industry, government and academia to ensure the safe, secure, reliable, and predictable integration of autonomous systems into society by covering the full spectrum of research and application across the three pillars of technology, ecosystem, and policy & governance.

For Students

Information of special interest to undergraduate and graduate students in computing.

For Researchers

Information of interest to faculty and researchers in computing research.

Programs and opportunities for women and minorities in computing research.

CRA Quadrennial Papers

Every four years the Computing Research Association, through its subcommittees, publishes a series of white papers called Quadrennial Papers that explore areas and issues around computing research with potential to address national priorities. The white papers attempt to portray a comprehensive picture of the computing research field detailing potential research directions, challenges, and recommendations.

From CRA Bulletin

Cra welcomes new members to its board of directors, cra welcomes tisdale fellow, congress meets robots: cra co-hosts senate robotics showcase and demo day, new cra practitioner-to-professor survey launched by cra-industry, nsf names greg hager assistant director for the computer and information science and engineering (cise) directorate, upcoming events, grand challenges for the convergence of computational and citizen science research, recently posted jobs, professional computer science phoenix stem instructional professor.

• University of Chicago

Professional Instructional Faculty in Computer Science and Engineering

• University of Minnesota

Professional Open Rank Faculty Position Statistics

• Stanford University

Professional Assistant or Associate Professor Position, Computational Psychology or Computational Neuroscience

• University of Maryland

Computing Research News

• CRA Update: We Want You! Volunteer Opportunities for 2024-2025
• CRA Congratulates New New AAA&S, AAAI, AAAS, ACM, and IEEE-CS Fellows
• CRA-WP Names Martez Mott as 2024 Skip Ellis Early Career Award Recipient
• CRA-WP Names Yakun Sophia Shao as 2024 Anita Borg Early Career Award Recipient
• Policy Spotlight: Juan E. Gilbert, University of Florida
• CRA Congratulates Newly Announced Deans at New Jersey Institute of Technology and Tulane University
• CERP Infographic: Where Are They Now? Exploring the Career Paths of Past Participants of Grad Cohort for IDEALS and Grad Cohort for Women
• CRA-E Announces New Co-Chair, Welcomes New Board Members, and Thanks Departing Members for Their Service
• Resources for Designing Undergraduate Research Experiences
• Summer Courses Are In Session: UR2PhD Is Supporting The Next Generation of Researchers
• UR2PhD Computing Research Engagement and Awareness Workshops Helped Undergraduates Learn More About Research Careers and Pathways
• CCC Chair Daniel Lopresti Transitions to Chair Emeritus and Several Council Members Rotate Off

Click here for all the articles!

Click Here to Subscribe to CRN

CRN is published ten times a year: January, February, March, April, May, June, August, September, October, November

From the CRA Policy Blog

• FY25 Appropriations Update: Senate Marks for NSF, NIST, NASA Released; Contains Increase for NSF, other Agencies, and Expands Funds for Chips Act Implementation August 5, 2024
• OSTP Releases Research Security Memo to Research Agencies; Begins Implementation Timeline August 1, 2024
• FY25 Appropriations Update: House Appropriators Recommend Flat Funding the Office of Science but Give a Healthy Increase to ASCR July 18, 2024

From the CCC Blog

• Former CCC Council Member Chad Jenkins Receives 2024 Richard Tapia Achievement Award August 5, 2024
• CCC Weekly Computing News: HPCWire Inaugural 35 Legends List Features Council Member Bill Gropp August 2, 2024
• New CCC Council Member Manish Parashar Receives 2024 CRA Distinguished Service Award August 1, 2024

• Advisers & Contacts
• Bachelor of Arts & Bachelor of Science in Engineering
• Prerequisites
• Declaring Computer Science for AB Students
• Declaring Computer Science for BSE Students
• Class of '25, '26 & '27 - Departmental Requirements
• Class of 2024 - Departmental Requirements
• COS126 Information
• Important Steps and Deadlines
• Independent Work Seminars
• Guidelines and Useful Information
• Undergraduate Research Topics
• AB Junior Research Workshops
• Undergraduate Program FAQ
• Minor Program
• Funding for Student Group Activities
• Mailing Lists and Policies
• Study Abroad
• Jobs & Careers
• Admissions Requirements
• Breadth Requirements
• Pre-FPO Checklist
• FPO Checklist
• M.S.E. Track
• M.Eng. Track
• Departmental Internship Policy (for Master's students)
• General Examination
• Fellowship Opportunities
• Travel Reimbursement Policy
• Communication Skills
• Course Schedule
• Course Catalog
• Research Areas

Interdisciplinary Programs

Technical reports, computing facilities.

• Researchers
• Technical Staff
• Administrative Staff
• Graduate Students
• Undergraduate Students
• Graduate Alumni
• Climate and Inclusion Committee
• Resources for Undergraduate & Graduate Students
• Outreach Initiatives
• Resources for Faculty & Staff
• Spotlight Stories
• Job Openings

Screen capture of sndpeek, real-time audio visualization software.

​Research Areas

Browse areas of research our faculty and students take part in.

Other programs related to Computer Science at Princeton.

View and search technical papers and reports with contributions from our faculty and students.

Details on the computing resources available to faculty and students.

The computer science department at the University of Virginia attracts federal research support in excess of $6 million annually, with total external research funding of more than $19.8 million each year. In addition to excelling in traditional research areas within computer science, we believe that many important research challenges lie at the boundary of computer science and other disciplines. 

Our Research

Computer systems, architecture, and networks.

Strength in computer systems has been a pillar of our department since its earliest days. This focus has led to notable contributions, for example, in cyber-physical systems, compilers, grid computing, and computer architecture.

Cyber-physical Systems

Cyber-physical Systems (CPS) and the Internet of Things have been identified by the National Academy of Sciences as national research priorities, critical to educating scientists and engineers for an increasingly cyber-enabled future. In response, we have created the multi-disciplinary Link Lab, bringing together researchers from the departments of CS, Electrical & Computer Engineering, Mechanical & Aerospace Engineering, and Engineering Systems & Environment.

Artificial Intelligence (AI)

The recent explosive growth in artificial intelligence (AI) and data mining demonstrates their vast potential to radically transform the norms and practices in every industry and every aspect of our society. Our department is advancing the state of the art in both fundamental algorithms as well as new algorithms to help users benefit from this revolution and provide new capabilities for the benefit of society.

UVA’s Department of Computer Science has a rapidly growing group of faculty working in the area of cyber security, including hardware, software, operating systems, networks, and cryptographic theory, working in close collaboration with other research groups such as the Link Lab, Software Engineering, and AI.

Software Engineering

Software-intensive systems play a variety of critical roles throughout society: they support human decision making in medical, transportation, and legal domains and, over the coming years they will increasingly operate autonomously – without a human in the loop. Our department’s strength in formal methods and program analysis allows us to advance the state of the art in all these areas, ensuring that these systems operate correctly and securely.

With our recent successful faculty hires in the CS department, the areas of security/cryptography, algorithmic game theory, as well as network science have achieved critical mass that puts CS@UVA in a unique position to differentiate itself and serve as a catalyst for rapid growth in this area.

CS Computing Education Center

Uva link lab, uva biocomplexity insitute and initiative, cyber innovation & society, computer science research news, uva engineering seeks applicants for talent pipeline.

UVA Engineering is recruiting talent for its Rising Scholars program.

‘Digital Twins’ Give Olympic Swimmers a Boost

In Scientific American, systems engineering and computer science alumni explain how a swimmer’s digital twin can take performance to record-breaking levels.

Before the ‘Rockets’ Red Glare’ of July 4, Celebrate UVA’s Spaceport Launch

UVA Rocketry’s Sabre 1 soared over 8,000 feet into a cloudy New Mexico sky in the team’s first foray in the world’s largest intercollegiate rocket design and engineering competition.

UVA and the Toyota Research Institute Aim To Give Your Car the Power To Reason

UVA computer scientist Yen-Ling Kuo is developing artificial intelligence to make your car a trusted partner and coach.

Microsoft Research: Advancing science and technology to benefit humanity

Microsoft Research podcast

What’s Your Story: Emre Kiciman 

GENEVA uses large language models for interactive game narrative design  

August 5, 2024 | Sudha Rao, Chris Brockett, Bill Dolan

Players, creators, and AI collaborate to build and expand rich game narratives  

Research Focus: Week of July 29, 2024  

July 31, 2024

Explore Microsoft Research Forum

Microsoft Research Forum  

Keynote: Building Globally Equitable AI  

Panel Discussion: Generative AI for Global Impact: Challenges and Opportunities  

Research Forum Brief | June 2024  

Careers in research, principal researcher – ai4science  .

Location : Beijing, China

Data & Applied Scientist II – Microsoft Power Platform  

Location : Bangalore, Karnataka, India

Data and Applied Scientist II – Office Experience Organization (OXO)  

Location : Hyderabad, Telangana, India

Data Scientist – Microsoft Bing  

Researcher – microsoft research asia  .

Locations : Beijing, China; Shanghai, China

Principal Data Scientist – Observability and Intelligence (OIC)  

Location : Suzhou, China

Data & Applied Scientist II – Bing Local Team  

Location : Barcelona, Spain

Data Scientist – Microsoft Teams Data Science team  

Location : Prague, Czech Republic

Senior Data Scientist – Microsoft Teams Data Science team  

Cambridge residency programme – ai for domains  .

Location : Cambridge, UK

Machine Learning Research Engineer – Strategic Planning and Architecture (SPARC) team  

Cambridge residency program – robotics  , senior data and applied scientist – health ai  .

Location : Herzliya, Tel Aviv, Israel

Principal Security Research Lead – Exposure Management  

Locations : Beer-Sheva, Israel; Haifa, Israel; Herzliya, Tel Aviv, Israel; Israel; Nazareth, Northern, Israel

Principal Security Research Manager – Microsoft Defender For Endpoint  

Location : Israel

Hybrid Cloud Security Researcher – EPSF IL  

Cloud security researcher – epsf il  , senior cloud security researcher – epsf il  , researcher  .

Location : Redmond, WA, US

Principal Research Scientist – Responsible & Open Ai Research (ROAR)  

Senior research scientist – vision and document understanding  , senior data & applied scientist – microsoft power platform  , senior data scientist – ads fraud detection  .

Locations : Mountain View, CA, US; Redmond, WA, US

Data Science II – Azure Core Trusted Platform team  

Locations : Redmond, WA, US; Remote (within US)

Events & conferences

Microsoft research forum | episode 4  .

Upcoming: September 3, 2024

News & awards

Bhaskar mitra receives two acm sigir early career researcher awards  .

ACM SIGIR  |  Jul 29, 2024

‘Enormous business potential’: Microsoft on why GraphRAG outperforms naive RAG  

The Stack  |  Jul 26, 2024

Amini receives “Rising Star” award at VentureBeat’s 6th Annual Women in AI awards  

VentureBeat  |  Jul 11, 2024

Sriram Rajamani at Microsoft Research on AI and deep tech in India  

Forbes India  |  Jun 28, 2024

• Follow on X
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram
• Subscribe to our RSS feed

Share this page:

• Share on Facebook
• Share on LinkedIn
• Share on Reddit

Carnegie Mellon University School of Computer Science

Research at scs.

Through many research and educational partnerships, SCS faculty exercise daily leadership in the fields of information technology, networking, cybersecurity, machine learning, natural language processing, speech recognition, robotics and more. They work closely with nonprofit agencies and industry clients to develop and mature technologies from concept through delivery to end-users. Our diverse interdisciplinary research also extends into areas not traditionally considered part of computer science. We invite you to explore our department websites to learn more about our research.

Departments

Ray and Stephanie Lane Computational Biology Department

Computer Science Department

Human-Computer Interaction Institute

Language Technologies Institute

Machine Learning Department

Robotics Institute

Software and Societal Systems Department

Additional Centers

Center for IDeaS

Center for Computational Analysis of Social and Organizational Systems

CyLab Security and Privacy Institute

Get To Know Us

• Meet Our Dean
• Read Our News
• HR Equal Opportunity Services
• SCS Tour Policy
• Office of International Education
• All About CMU
• Why Pittsburgh?

• Research & Faculty
• Offices & Services
• Information for:
• Faculty & Staff
• News & Events
• Contact & Visit
• About the Department
• Message from the Chair
• Computer Science Major (BS/BA)
• Computer Science Minor
• Machine Learning and Data Science Minor
• Combined BS (or BA)/MS Degree Program
• Intro Courses
• Special Programs & Opportunities
• Student Groups & Organizations
• Undergraduate Programs
• Undergraduate Research
• Senior Thesis
• Peer Mentors
• Curriculum & Requirements
• MS in Computer Science
• PhD in Computer Science
• Admissions FAQ
• Financial Aid
• Graduate Programs
• Courses Collapse Courses Submenu
• Research Overview
• Research Areas
• Systems and Networking
• Security and Privacy
• Programming Languages
• Artificial Intelligence
• Human-Computer Interaction
• Vision and Graphics
• Groups & Labs
• Affiliated Centers & Institutes
• Industry Partnerships
• Adobe Research Partnership
• Center for Advancing Safety of Machine Intelligence
• Submit a Tech Report
• Tech Reports
• Tenure-Track Faculty
• Faculty of Instruction
• Affiliated Faculty
• Adjunct Faculty
• Postdoctoral Fellows
• PhD Students
• Outgoing PhDs and Postdocs
• Visiting Scholars
• News Archive
• Weekly Bulletin
• All Public Events
• Seminars, Workshops, & Talks
• Distinguished Lecture Series
• CS Colloquium Series
• CS + X Events
• Tech Talk Series
• Honors & Awards
• External Faculty Awards
• University Awards
• Department Awards
• Student Resources
• Undergraduate Student Resources
• MS Student Resources
• PhD Student Resources
• Student Organization Resources
• Faculty Resources
• Postdoc Resources
• Staff Resources
• Purchasing, Procurement and Vendor Payment
• Expense Reimbursements
• Department Operations and Facilities
• Initiatives
• Student Groups
• CS Faculty Diversity Committee
• Broadening Participation in Computing (BPC) Plan
• Northwestern Engineering

Andrew Crotty Receives Google Research Scholar Program Award

The award will support research advancing deep learning algorithms for compressing large datasets.

In the big data era, algorithms for efficient data compression have become increasingly crucial, both for transmission over the network and long-term archival. Most traditional compression techniques, however, treat data merely as sequences of bytes, missing the inherent relationships that exist in real-world datasets.

Consider a dataset containing customer shipping addresses, for example. The city and state can be uniquely inferred from the ZIP code, meaning these fields can be safely discarded without losing any information. Crotty’s work aims to identify these types of relationships automatically, even when they are subtle or complex, to help compress datasets in ways that were previously impossible.

The Google Research Scholar Program supports the advancement of world-class research by early-career faculty members in fields including algorithms and optimization, human-computer interaction, machine learning and data mining, natural language processing, privacy, quantum computing, security, and systems. Through the program, Google aims to facilitate connections among junior faculty and encourage the formation of long-term collaborative relationships.

Crotty will use the award during the 2024 academic year for his project titled “Deep Semantic Modeling for Compressing and Querying Big Data,” which builds on the DeepSqueeze deep semantic compression framework that he began working on as a postdoctoral researcher at Brown University.

DeepSqueeze uses a type of deep neural network called an autoencoder to efficiently capture complex relationships among categorical and numerical attributes in large datasets. The tool also supports guaranteed error bounds for lossy compression of numerical data and integrates seamlessly with common columnar compression formats.

“Our experimental evaluation used real-world datasets to demonstrate that DeepSqueeze can achieve more than a 4X reduction in size compared to state-of-the-art alternatives,” Crotty said.

Crotty aims to advance the functionality of DeepSqueeze by allowing users to directly query the compressed data without first having to decompress it, which can be expensive.

“Our work could have tangible benefits for widely used object storage and data warehousing systems, including Google Cloud Storage and BigQuery,” Crotty said.

Prior to joining Northwestern in fall 2022, Crotty was a postdoctoral researcher jointly appointed in the computer science departments at Carnegie Mellon University and Brown University. Previously, he served as a postdoctoral researcher in the Data Science Initiative at Brown University, where he also earned a PhD in computer science in 2019.

McCormick News Article

• Engineering Home
• CS Department
• News Article

Recent Stories

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

• Speakers & Mentors

Introduction to Computing Research (ICR)

Established in 2021, the Introduction to Computing Research (ICR) is a program with the mission to introduce undergraduate students to various areas of computing research and career options in those areas. Our goal is to provide equal access to exploratory research opportunities. ICR consists of two main components: (a) virtual workshops and (b) research internships.

Virtual Workshops

The ICR program offers a series of workshops to introduce undergraduates to various areas and career options in computing research. The workshop talks will have three main types:

Area overview talks: These talks introduce an area or a sub-area of computing research and research opportunities in that area. Example: a broad introduction to cloud computing and open research questions related to it.

Lab overview talks: In these talks, leading researchers introduce the research in their own labs. Example: an overview of the research on theoretical computer science at Hopkins.

Career talks: These talks give advice about applying to grad school, life as a grad student, joining industry after a PhD in computer science, etc.

The forth round of ICR workshops will hybrid events, with in-person workshops in Baltimore City.

• April 20, 2023, 1-5PM EST
• April 26, 2021, 2-3:50PM EST
• May 26, 2021, 2-5PM EST
• June 24, 2021, 2-5PM EST

Register to Attend ICR'23 Workshops!

Research Internships

ICR offers an eight-week internship program that provides an opportunity for undergraduate students to gain hands-on research experience in computing while conducting research under the guidance of faculty members, postdoctoral fellows, and advanced graduate students.

• Advising by a faculty member
• A postdoctoral fellow or advanced graduate student mentor
• Hands-on research experience in computing research or an interdisciplinary area
• Potential opportunity to contribute to and co-author a scientific paper and/or build a software system
• 1:1 career counseling
• A bi-weekly stipend
• Prof. Venkataraman's lab
• Prof. Huang's lab
• Dr. Wassenberg's group
• Prof. Fan's lab
• Prof. Unberath's lab
• Prof. Dinitz's lab

Archana Venkataraman

• Area: Functional Neuroimaging (fMRI, EEG), Machine Learning & Probabilistic Inference, Network Modeling of the Brain, Integration of Imaging, Genetics and Behavioral Data
• Talk title: Engineering Solutions for Brain Dysfunction
• Talk type: Lab overview
• Research internship opening!
• Link to the talk: TBD

Chien Ming Huang

• Area: Human-Robot Interaction, Human-Computer Interaction, Robotics
• Talk title: Designing Robotic Technology for People
• Talk type: Lab overview, Career talk

Jan Wassenberg

• Area: algorithms and theory, distributed systems and parallel computing, machine perception, security, privacy, and abuse prevention
• Talk title: Thoughts on CS research, career
• Talk type: Career talk
• Area: Biomedical Data Science, Computational Medicine, Genomics and Systems Biology
• Talk title: Interactive analysis and visualization of spatial transcriptomics data
• Potential research internship opening!

Mathias Unberath

• Area: Imaging Systems, Machine Learning, Human-AI Interaction, Augmented Reality
• Talk title: Advancing Healthcare with Artificial Intelligence: The Good, the Bad, and the Ugly
• Talk type: Area overview, Lab overview

Michael Dinitz

• Area: Theoretical computer science
• Talk title: Theory of Network Design Problems

Yashar Ganjali

• Area: Systems and networking
• Talk title: Essentialism in Graduate School

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Computer science articles within Scientific Reports

Article 05 August 2024 | Open Access

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions

• Ruixue Yuan
• , Yangfen Chen
•  &  Yingying Zheng

Zero-shot stance detection based on multi-expert collaboration

• Xuechen Zhao
• , Guodong Ma
•  &  Jinfeng Miao

Entanglement detection with classical deep neural networks

• Julio Ureña
• , Antonio Sojo
•  &  Daniel Manzano

Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT

• Hani Alshahrani
• , Mohd Anjum
•  &  Asadullah Shaikh

Article 04 August 2024 | Open Access

Dense object detection methods in RAW UAV imagery based on YOLOv8

• , Xinfa Wang
•  &  Jianping Wang

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

• Chandrakanta Mahanty
• , S Gopal Krishna Patro
•  &  Wahaj Ahmad Khan

Article 03 August 2024 | Open Access

An efficient blockchain-based framework for file sharing

• Wanzong Peng
• , Tongliang Lu
•  &  Zhongpan Wang

WBi-YOLOSF: improved feature pyramid network for aquatic real-time target detection under the artificial rabbits optimization

• Liubing Jiang
•  &  Yongman Wu

Article 02 August 2024 | Open Access

Identifying sex from pharyngeal images using deep learning algorithm

• Hiroshi Yoshihara
• , Memori Fukuda
•  &  Yusuke Tsugawa

Global attractive set for quaternion-valued neural networks with neutral items

• , Zhengwen Tu
•  &  Dandan Wang

Elicitation of security threats and vulnerabilities in Insurance chatbots using STRIDE

• Zilungile Bokolo
•  &  Olawande Daramola

MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance

• Abdelhak Bouchakour
• , Laid Zarour
•  &  Sherif S. M. Ghoneim

Article 01 August 2024 | Open Access

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

A new possibilistic-based clustering method for probability density functions and its application to detecting abnormal elements.

• Hung Tran-Nam
• , Thao Nguyen-Trang
•  &  Ha Che-Ngoc

AI-enhanced real-time cattle identification system through tracking across various environments

• Su Larb Mon
• , Tsubasa Onizuka
•  &  Thi Thi Zin

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine

• Ritesh Maurya
• , Satyajit Mahapatra
•  &  Nageshwar Nath Pandey

The analysis of educational informatization management learning model under the internet of things and artificial intelligence

• , Xinliang Long
•  &  Kunli Wang

Article 31 July 2024 | Open Access

Detection and prediction of anomalous behaviors of enterprise’s employees based on data-mining and optimization algorithm

•  &  Yutong Meng

Dual-scale shifted window attention network for medical image segmentation

• , Xiao-lei Yin
•  &  Zhao-yuan Ma

Power supply characterization of baseload and flexible enhanced geothermal systems

• Mohammad J. Aljubran
•  &  Roland N. Horne

Mixed T-domain and TF-domain Magnitude and Phase representations for GAN-based speech enhancement

• , Zhang Yang
•  &  Wang Shiyuan

Advanced differential evolution for gender-aware English speech emotion recognition

•  &  Jiulong Zhu

Article 30 July 2024 | Open Access

Adaptive task migration strategy with delay risk control and reinforcement learning for emergency monitoring

• Zhiyong Fan
• , Yuanmo Lin
•  &  Hang Xu

A geometric approach for accelerating neural networks designed for classification problems

• Mohsen Saffar
• , Ahmad Kalhor
•  &  Ali Habibnia

Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books

• , Caixia Mao
•  &  Tao Qian

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

• Jonas Van Der Donckt
• , Nicolas Vandenbussche
•  &  Sofie Van Hoecke

An improved YOLOv8 safety helmet wearing detection network

• Xudong Song
• , Tiankai Zhang
•  &  Weiguo Yi

A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering

• Shuhai Wang
• , Linfu Sun
•  &  Yang Yu

Personalized rehabilitation approach for reaching movement using reinforcement learning

• Avishag Deborah Pelosi
• , Navit Roth
•  &  Anat Dahan

Article 29 July 2024 | Open Access

Adaptive median filter salt and pepper noise suppression approach for common path coherent dispersion spectrometer

• Shouxin Guan
•  &  Ruyi Wei

Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias

• Jiaqing Zhang
• , Sabyasachi Bandyopadhyay
•  &  Parisa Rashidi

Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study

• Allan J. Kember
• , Hafsa Zia
•  &  Elham Dolatabadi

An attention-based deep learning for acute lymphoblastic leukemia classification

• Malathy Jawahar
• , L. Jani Anbarasi
•  &  Amir H. Gandomi

Extended dipeptide composition framework for accurate identification of anticancer peptides

• Faizan Ullah
• , Abdu Salam
•  &  Taha Alfakih

Article 28 July 2024 | Open Access

The intelligent evaluation in ice and snow tourism based on LSTM network

• , Hailong Yuan
•  &  Tian Hu

Boundary guidance network for medical image segmentation

•  &  Cheng Yang

Article 27 July 2024 | Open Access

A reversible-zero watermarking scheme for medical images

•  &  Ateeq Ur Rehman

Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

• Jiguang Chen
•  &  Yuxin Qin

Article 26 July 2024 | Open Access

Semi-autonomous touch method merging robot’s autonomous touch and user-operated touch for improving user experience in robot touch

• Ryoma Mitsuoka
• , Tomonori Kubota
•  &  Kohei Ogawa

Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification

• SK Khaja Shareef
• , R. Krishna Chaitanya
•  &  Ramesh Vatambeti

Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India

• G. H. Harish Nayak
• , Md Wasi Alam
•  &  Chandan Kumar Deb

Code smells analysis for android applications and a solution for less battery consumption

• Aakanshi Gupta
• , Bharti Suri
•  &  Luis Fernandez-Sanz

Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method

• , Yanrong Huang
•  &  Qi Tao

Article 25 July 2024 | Open Access

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

• Sainan Zhang
•  &  Jisung Song

A hybrid recommendation algorithm based on user nearest neighbor model

• , Jiabin Wang
•  &  Penggui Yan

Article 24 July 2024 | Open Access

Secure computation protocol of Chebyshev distance under the malicious model

• , Weitong Chen
•  &  Xiaomeng Liu

Efficient detection of driver fatigue state based on all-weather illumination scenarios

• , Qihuang Gao
•  &  Jianbiao He

Article 23 July 2024 | Open Access

Impact of discrepant accumulations strategy on collective cooperation in multilayer networks

•  &  Lili Li

Article 22 July 2024 | Open Access

Prediction of HPC compressive strength based on machine learning

•  &  Pin Zhou

YOLO-Granada: a lightweight attentioned Yolo for pomegranates fruit detection

• , Chenfan Du
•  &  Rolla Almodfer

Browse broader subjects

• Systems biology
• Mathematics and computing

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Office of the Vice President for Research

Computer science faculty member earns nsf career award.

Rishab Nithyanand , assistant professor in the Department of Computer Science, will tackle the enforcement of consumer privacy regulations with the help of a CAREER award from the National Science Foundation (NSF). 

The award, the NSF’s most prestigious award in support of early-career faculty, includes a five-year grant for $633,023. Nithyanand and his team will develop a variety of computational tools to help enforcement agencies perform large-scale audits to determine if organizations are complying with consumer privacy regulations.  

“One of the things you realize as you delve into regulatory enforcement, particularly with privacy, is that agencies really aren’t well staffed or well resourced,” Nithyanand said. “ We want to build computational tools to make their investigations and operations more efficient and scalable. ” 

Unlike current automated methods, which gather data first and then determine if the organization is following regulations, Nithyanand will build an auditing framework based on current mandates in privacy regulations to determine the likelihood that an organization is violating the law without accessing their internal data. 

“We’re not suggesting we can replace human investigators,” he said. “We’re hoping to make their work more efficient by pointing out exactly what they should focus their investigation on.”

In addition to identifying privacy law violations, Nithyanand will develop low-cost mechanisms to let organizations know about potential violations and rectify them without expending precious enforcement agency resources. 

The project comes at a critical time— 18 states have data privacy laws on the books , and seven states will begin enforcement in 2025. Nithyanand will create tools that can be adjusted to the specifics of each state’s legislation. 

The award will also support Nithyanand in growing his trailblazing collaboration with the College of Law.  For three years, Nithyanand and law professor Mihailis Diamantis have co-taught Privacy Law and Technology, which enrolls both graduate computer science and law students. They also co-lead the  Sentinels for Privacy-Aware and Responsible Technological Advancement (SPARTA) , the first lab to allow research assistants from the Department of Computer Science and the College of Law to work side by side on internet privacy studies. 

  This spring the pair began expanding their interdisciplinary efforts by integrating legal education into undergraduate computer science curriculum. 

“This law and computer science collaboration is very unique,” Nithyanand said. “We are one of a handful of American universities, possibly the first, to offer computer science and law students the opportunity to take the same data privacy course side by side and to pursue research on these issues beyond the classroom together.”

Professor Mohit Gupta’s research “sees the world in a new light,” one photon at a time

By Rachel Robey

Professor Mohit Gupta’s work on single photon imaging gains unprecedented traction as industry interest rises.

It’s a tale as old as start-ups: The world’s greatest inventions always begin in a garage.

Such was the case for Computer Sciences (CS) Professor Mohit Gupta, whose research in single-photon cameras (SPCs) and single photon avalanche diodes (SPADs) had beginnings in his garage along with his lab. Now a career-defining offshoot of his research in computer vision, Gupta’s exploration of these devices started purely as curiosity. 

It’s easy to see why: Single photon imaging devices are capable of detecting light down to individual photons. “As academics, we’re often concerned with what happens when we push something to its limit,” says Gupta. “For imaging, that’s detecting individual photons. There’s nothing beyond that.” 

Initial tests were rudimentary. A single-photon camera imaged Gupta slowly cycling around his unlit garage to test the camera’s capabilities in low light and high movement conditions. During a particularly memorable late-night experiment, concerned neighbors who were unfamiliar with Gupta’s work almost called the police on him as he drove through the neighborhood with the research camera strapped to the dashboard of his car; another time, a police car actually flashed lights and stopped Gupta and researchers for questioning while performing late night experiments. The results of these early experiments — which amounted to just a few pixels — were nevertheless promising.

At WISION Lab, single-photon cameras “see the world in a new light”

Unlike typical consumer cameras that perform best in bright conditions, ultra sensitive single-photon cameras are able to detect light down to its most fundamental unit: the photon. Not only does this make SPCs more flexible and capable across a spectrum of light conditions, but it allows researchers like Gupta to pursue answers right up against the foundational laws of physics .

“These cameras will allow us to capture the visual world around us to the maximum fidelity that physics allows,” Gupta continues. “If you’re able to consider every photon, then that’s virtually all the information there is.” In a sense, the images SPCs create are the truest possible representations of reality.

Naturally, the possibilities for this kind of technology are endless. With their impressive dynamic range and suitability for suboptimal visual conditions, SPCs are a plausible solution for situations ranging from extreme robotics (for example, a robot exploring an underground cave) to fluorescence lifetime microscopy (an imaging technique used in cancer research) or even in virtual reality (VR) headset sensors. 

At Ubicept , a Boston-based start-up exploring SPCs where Gupta serves as a founding advisor, development of a “ new kind of camera that sees in the dark (and around corners) ” is especially promising for self-driving vehicles. Over at the Computational Optics Group within the Department of Biostatistics and Medical Informatics , Professor Andreas Velten , a close collaborator of Gupta’s and likewise a founding advisor for Ubicept, researches the biomedical imaging and remote sensing applications of this technology . Meanwhile, Canon’s recent release — a $25,000 SPAD device — is intended for high security and surveillance applications.

But before any of these possibilities are realized, more research and development are needed. With industry backing, it’s happening faster than ever.

With industry support, innovation at the speed of light

Years ago when Sony, an industry leader in photosensors, unveiled their Faculty Innovation Award supporting “pioneering research,” Gupta decided to submit his work . “I sent a proposal, and it landed in the right place with the right people,” says Gupta, who won the award in 2020 and received additional funding in 2021. “It started off as a one year thing, but now [the  collaboration with Sony ] is in its fourth year.”  

Around the same time that Gupta first submitted his proposal, big names including Sony, Canon, and Apple all began to invest heavily in the nascent single photon imaging technology. A veritable space race ensued, causing research in the area to explode. Seven years ago, Gupta’s lab had access to just one pixel that cost more than $10,000. Today, SPAD technology is already a consumer device that’s been tucked inside the latest generations of iPhone.

“That level and speed of progress is almost unheard of with a technology like this,” says Gupta, who uses research cameras with nearly a quarter megapixels.

Given the technical difficulties presented by SPCs, government investment and industry support will be necessary to aid the research required to fully develop the technology. “One major practical hurdle for these kinds of sensors is they consider every individual photon that comes in,” says Gupta. “In a second you may see trillions of photons, so for these things to ever be practical, we need ways to deal with this data deluge.” For Gupta, this means going back to first principles to rethink algorithms related to computer vision, machine learning, and image processing.

“Here at UW, we’re fortunate to have partners like WARF. They really push people,” says Gupta, who has worked with WARF on securing nearly 25 patents since joining the CS faculty in 2016. “For me, the most encouraging thing is that a large fraction of [our patents] aren’t just gathering dust – they’ve actually been licensed for practical use by companies.” 

Yet while the licensing, patent granting, and industry investing are welcome encouragement, Gupta’s core motivation remains the same as ever: to always follow curiosity and explore novel ways of “seeing the world in a new light.”

Jin-Hee Cho named the Stephen and Cherye Tyndall Moore Computer Science Junior Faculty Fellow

The Virginia Tech Board of Visitors named Cho the Stephen and Cherye Tyndall Moore Computer Science Junior Faculty Fellow.

• Mark Owczarski
• Share on Facebook
• Share on Twitter
• Copy address link to clipboard

Jin-Hee Cho , associate professor of computer science in the College of Engineering at Virginia Tech, has been named the Stephen and Cherye Tyndall Moore Computer Science Junior Faculty Fellow by the Virginia Tech Board of Visitors . 

The fellowship was established with a gift from Stephen and Cherye Tyndall Moore and enables the Department of Computer Science to recruit, reward, and retain outstanding faculty. Recipients hold the fellowship for five years. 

A member of the Virginia Tech community since 2018 , Cho has excelled in research, teaching, and student mentoring in the area of cybersecurity. Her work features a transformative approach to uncertainty-aware decision-making that integrates machine learning with belief/evidence theory, addresses pivotal security challenges, and significantly advances the research frontier in artificial intelligence for cybersecurity.

Cho’s research has been supported by $6.5 million in external funding. She has authored 67 journal papers and 97 peer-reviewed conference publications and has more than 6,990 citations. Six master’s degree and two Ph.D. students have graduated under her mentorship, and she continues to advise an active research group consisting of six Ph.D. students and multiple graduate and undergraduate students. 

Cho regularly teaches cybersecurity and theory courses to graduate students located in the greater Washington, D.C., metro area and in Blacksburg. She is a researcher with the Commonwealth Cyber Initiative and is affiliate faculty at the Virginia Tech National Security Institute . Cho is a member of the editorial boards of three journals and has served on technical program committees for several major conferences in her field. She recently led a research workshop on AI for Social Good.

A senior member of the Institute of Electrical and Electronics Engineers and a member of the Association for Computing Machinery, Cho received her bachelor’s degree from Ewha Womans University in South Korea , a master’s degree from Washington University in St. Louis, and master’s degree and Ph.D. in computer science from Virginia Tech.

Chelsea Seeber

540-231-2108

• Artificial Intelligence Frontier
• College of Engineering
• Commonwealth Cyber Initiative
• Computer Science
• Cybersecurity
• Named chairs, professorships, and fellowships
• Virginia Tech Global Distinction

Related Content

Advertisement

Supported by

Tech Bosses Preach Patience as They Spend and Spend on A.I.

Big technology companies show no signs of slowing their spending on artificial intelligence, even though a payoff still looks a long way away.

• Share full article

By Karen Weise

Karen Weise covers technology from Seattle.

Mark Zuckerberg, Meta’s chief executive, started 2023 by declaring it the “year of efficiency.” Like several of its big tech peers, Meta cut jobs and mothballed expansion plans.

Then came A.I.

Mr. Zuckerberg started this year saying his company would spend more than $30 billion in 2024 on new tech infrastructure. In April, he raised that to $35 billion. On Wednesday, he increased it to at least $37 billion. And he said Meta would spend even more next year.

Mr. Zuckerberg said he’d rather build too fast “rather than too late,” and allow his competitors to get a big lead in the A.I. race.

The tech industry’s biggest companies have made it clear over the last week that they have no intention of throttling their stunning levels of spending on artificial intelligence, even though investors are getting worried that a big payoff is further down the line than once thought.

In the last quarter alone, Apple, Amazon, Meta, Microsoft and Google’s parent company Alphabet spent a combined $59 billion on capital expenses, 63 percent more than a year earlier and 161 percent more than four years ago. A large part of that was funneled into building data centers and packing them with new computer systems to build artificial intelligence. Only Apple has not dramatically increased spending because it does not build the most advanced A.I. systems itself.

Big Tech Spending Has Shot Up

Capital expenditures in the latest quarter were up 63 percent from a year earlier.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .

We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here . By continuing to use our site, you accept our use of cookies, revised Privacy Policy and Terms of Service .

New to Zacks? Get started here.

Member Sign In

Don't Know Your Password?

• Zacks #1 Rank
• Zacks Industry Rank
• Zacks Sector Rank
• Equity Research
• Mutual Funds
• Mutual Fund Screener
• ETF Screener
• Earnings Calendar
• Earnings Releases
• Earnings ESP
• Earnings ESP Filter
• Stock Screener
• Premium Screens
• Basic Screens
• Thematic Screens
• Research Wizard
• Personal Finance
• Money Management
• Retirement Planning
• Tax Information
• My Portfolio
• Create Portfolio
• Style Scores
• Testimonials
• Zacks.com Tutorial

Services Overview

• Zacks Ultimate
• Zacks Investor Collection
• Zacks Premium

Investor Services

• ETF Investor
• Home Run Investor
• Income Investor
• Stocks Under $10
• Value Investor
• Top 10 Stocks

Other Services

• Method for Trading
• Zacks Confidential

Trading Services

• Black Box Trader
• Counterstrike
• Headline Trader
• Insider Trader
• Large-Cap Trader
• Options Trader
• Short Sell List
• Surprise Trader
• Alternative Energy

You are being directed to ZacksTrade, a division of LBMZ Securities and licensed broker-dealer. ZacksTrade and Zacks.com are separate companies. The web link between the two companies is not a solicitation or offer to invest in a particular security or type of security. ZacksTrade does not endorse or adopt any particular investment strategy, any analyst opinion/rating/report or any approach to evaluating individual securities.

If you wish to go to ZacksTrade, click OK . If you do not, click Cancel.

Image: Bigstock

How to Play Super Micro Computer (SMCI) Stock Before Q4 Earnings?

Super Micro Computer ( SMCI Quick Quote SMCI - Free Report ) is scheduled to report fourth-quarter fiscal 2024 results on Aug 6. For the fiscal fourth quarter, the company expects revenues between $5.1 billion and $5.5 billion. The Zacks Consensus Estimate for the same is pegged at $5.3 billion, indicating growth of 142.6% from the year-ago quarter’s reported value. Super Micro Computer expects non-GAAP earnings per share between $7.62 and $8.42. The Zacks Consensus Estimate for earnings is pegged at $8.08 per share, suggesting a rise of 130.2% from the year-ago reported figure. The figure has been unchanged over the past 30 days.

SMCI has an impressive earnings surprise history. In the last reported quarter, the company delivered an earnings surprise of 15.05%. Its earnings beat the Zacks Consensus Estimate in the trailing four quarters, the average surprise being 6.96%.

Earnings Whispers

Our proven model does not conclusively predict an earnings beat for Super Micro Computer this time around. The combination of a positive Earnings ESP and a Zacks Rank #1 (Strong Buy), 2 (Buy) or 3 (Hold) increases the odds of an earnings beat. This is not the case here. You can uncover the best stocks to buy or sell before they are reported with our Earnings ESP Filter . Super Micro Computer has an Earnings ESP of 0.00% and a Zacks Rank #3 at present. You can see the complete list of today’s Zacks #1 Rank stocks here .

Key Factors to Consider

Super Micro Computer has been capitalizing on the current AI boom on the back of robust demand for its server and storage solutions. Especially, growing OEM component orders have been leading to a spike in the uptake of its AI servers. This is expected to have been a tailwind for the company in the quarter under review. SMCI’s strong investments in high-quality, optimized Direct Liquid Cooling (DLC) solutions for high-end applications are anticipated to have benefited the quarterly performance. The company’s strengthening manufacturing capabilities to support growth of AI and enterprise rack-scale liquid-cooled solutions, and capitalize on the rising demand for liquid-cooled data centers are likely to have been positives.

Super Micro Computer’s solid traction across top-tier data centers, emerging cloud service providers, enterprise/channel, and edge/IoT/telco customers, owing to its robust next-generation AI and CPU platforms, is expected to have been a positive. SMCI’s robust building block architecture and operation/production automation systems, offering optimized rack-scale solutions with time-to-market and quality advantages to its customers, are expected to have been other tailwinds. These factors are expected to have aided the performance of Super Micro Computer’s Server & Storage Systems segment.  Also, the company’s robust portfolio of infrastructure solutions for 5G and telecom workloads on the back of its continuing partnership with NVIDIA ( NVDA Quick Quote NVDA - Free Report ) is likely to have contributed well to the segment’s top-line growth in the fiscal fourth quarter.  The Zacks Consensus Estimate for Server & Storage System revenues is pegged at $5.02 billion, indicating a significant jump of 147% from the year-ago quarter’s reported figure. Strong momentum across its H100-based systems and AI inferencing systems are likely to have boosted the performance of the Subsystems & Accessories segment in the fiscal fourth quarter. The Zacks Consensus Estimate for fourth-quarter fiscal 2024 Subsystems and accessories revenues is pegged at $281 million, indicating a rise of 83.7% from the year-ago quarter’s reported figure.

Price Performance & Valuation

Super Micro Computer’s shares have gained 119.8% on a year-to-date basis, outperforming the industry , the Zacks Computer & Technology sector and the S&P 500 index’s rise of 47.7%, 14.3% and 12.4%, respectively. The company has also outpaced the 9.3% and 52.3% rallies of its peers Western Digital ( WDC Quick Quote WDC - Free Report ) and Pure Storage ( PSTG Quick Quote PSTG - Free Report ) , respectively, in the year-to-date period.

Year-to-Date Price Performance

Now, let us look at the value that Super Micro Computer offers to its investors at current levels. Currently, SMCI is trading at a premium with a trailing 12-month P/E of 18X compared with the industry’s 17.92X, reflecting a slightly stretched valuation at present.

Investment Thesis

Super Micro Computer’s diverse business model, which encompasses Graphics Processing Units, AI, core computing, storage, 5G telco, edge and Internet of Things solutions, is expected to drive its long-term prospects. Its expanding pipeline of AI-backed software solutions positions it well to capitalize on the ongoing AI boom. However, near-term macroeconomic uncertainties and supply-chain challenges, especially in DLC-related components, are concerning for the company. High inflation, geo-political tensions and rising competition do not bode well. Given the combination of both risks and rewards, we suggest the existing SMCI shareholders maintain their positions. However, new investors may consider waiting for a more favorable entry point for Super Micro Computer. Stay on top of upcoming earnings announcements with the Zacks Earnings Calendar .

See More Zacks Research for These Tickers

Pick one free report - opportunity may be withdrawn at any time.

Western Digital Corporation (WDC) - free report >>

NVIDIA Corporation (NVDA) - free report >>

Super Micro Computer, Inc. (SMCI) - free report >>

Pure Storage, Inc. (PSTG) - free report >>

Published in

This file is used for Yahoo remarketing pixel add

Due to inactivity, you will be signed out in approximately:

$1.4 million awarded for Alzheimer’s disease research training (UCI News)

Funds will support projects with significant clinical translational potential.

• Share on Facebook
• Share on Twitter
• Share on LinkedIn
• Share through Email
• Copy permalink

With a five-year, $1.4 million National Institutes of Health grant, the Center for Neural Circuit Mapping will establish a training program for Alzheimer’s disease research. An interdisciplinary team of 29 faculty members will mentor and support student trainees, employing state-of-the-art approaches to expand the mechanistic understanding of brain disorders through neural circuit mapping. From above left, they include: Chen Li , Xiaoyu Shi, Kalpna Gupta, Gopi Meenakshisundaram , Orkide Koyuncu, Mark Fisher, Xiangmin Xu, Christine Gall, Todd Holmes, Kei Igarashi, Zhaoxia Yu , Kevin Beier and Gregory Brewer.

“The program will emphasize the development of novel tools and methodologies for early detection, diagnosis and treatment of Alzheimer’s disease and other dementias,” said principal investigator Xiangmin Xu, Chancellor’s Professor of anatomy & neurobiology and director of the CNCM.

This story originally appeared in UCI News.

Related Posts

Smart health research to reduce cancer risks, ics students win 2024 gen ai hackathon, pierre baldi receives 2023 inns dennis gabor award, alumni spotlight: douglas schmidt’s unique career path to protecting the u.s., accessing stem in higher education, ics commencement 2024: tomorrow’s tech leaders.

Research on the color characteristics of embroidery patterns of Zhuang floral motifs based on clustering algorithm

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, index terms.

Computing methodologies

Computer graphics

Image manipulation

Image processing

Recommendations

Computational intelligence techniques for colour clustering.

Colour clustering (or colour quantisation) is an elementary image processing process to reduce the number of distinct colours in an image. Colour quantised images contain a colour palette and it is the entries in this palette that determine the quality ...

Histogram-based colour image fuzzy clustering algorithm

In this work, a histogram-based colour image fuzzy clustering algorithm is proposed for addressing the problem of low efficiency due to computational complexity and poor clustering performance. Firstly, the presented scheme constructs the red, green and ...

Research on a Skin Color Detection Algorithm Based on Self-adaptive Skin Color Model

Skin color detection is an important subject in computer vision research. Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. To aim at existing skin color ...

Information

Published in.

Association for Computing Machinery

New York, NY, United States

Publication History

Permissions, check for updates.

• Research-article
• Refereed limited

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View Options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

View options.

View or Download as a PDF file.

View online with eReader .

HTML Format

View this article in HTML Format.

Share this Publication link

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.