Computer Science
Showing 101-135 of 135 Results
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Mehran Sahami
Tencent Chair of the of the Computer Science Department, James and Ellenor Chesebrough Professor and Senior Fellow, by courtesy, at the Freeman Spogli Institute for International Studies
BioMehran Sahami is Tencent Chair of the Computer Science Department and the James and Ellenor Chesebrough Professor in the School of Engineering. As a Professor (Teaching) in the Computer Science department, he is also a Bass Fellow in Undergraduate Education and previously served as the Associate Chair for Education in Computer Science. Prior to joining the Stanford faculty, he was a Senior Research Scientist at Google. His research interests include computer science education, artificial intelligence, and ethics. He served as co-chair of the ACM/IEEE-CS joint task force on Computer Science Curricula 2013, which created curricular guidelines for college programs in Computer Science at an international level. He has also served as chair of the ACM Education Board, an elected member of the ACM Council, and was appointed by California Governor Jerry Brown to the state's Computer Science Strategic Implementation Plan Advisory Panel.
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J Kenneth Salisbury, Jr.
Professor (Research) of Computer Science and of Surgery (Anatomy), Emeritus
BioSalisbury worked on the development of the Stanford-JPL Robot Hand, the JPL Force Reflecting Hand Controller, the MIT-WAM arm, the PR-2 personal robot and the da Vinci Surgical Robot. His work with haptic interface technology led to the founding of SensAble Technology, producers of the PHANToM haptic interface and software. He also worked on developing telerobotic systems for dexterity enhancement in the operating room. His current research focuses on minimalist force controllable robots, human-machine interaction, cooperative haptics, medical robotics, and surgical simulation.
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Ludwig Schmidt
Assistant Professor of Computer Science
BioLudwig Schmidt is an assistant professor at Stanford University in the Computer Science Department and Stanford Data Science. Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, multimodality, and language models. Recently, Ludwig’s research group contributed to open source machine learning by creating OpenCLIP, DCLM, and the LAION-5B dataset. Ludwig completed his PhD at MIT and was a postdoc at UC Berkeley. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.
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Nigam H. Shah, MBBS, PhD
Professor of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsWe answer clinical questions using aggregate patient data at the bedside. The Informatics Consult Service (https://greenbutton.stanford.edu/) put this idea in action and led to the creation of Atropos Health. We build predictive models that allow taking mitigating actions, keeping the human in the loop.
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Yoav Shoham
Professor of Computer Science, Emeritus
BioYoav Shoham is professor emeritus of computer science at Stanford University. A leading AI expert, Prof. Shoham is Fellow of AAAI, ACM and the Game Theory Society. Among his awards are the IJCAI Research Excellence Award, the AAAI/ACM Allen Newell Award, and the ACM/SIGAI Autonomous Agents Research Award. His online Game Theory course has been watched by close to a million people. Prof. Shoham has founded several AI companies, including TradingDynamics (acquired by Ariba), Katango and Timeful (both acquired by Google), and AI21 Labs. Prof. Shoham also chairs the AI Index initiative (www.AIindex.org), which tracks global AI activity and progress, and WeCode (www.wecode.org.il), a nonprofit initiative to train high-quality programmers from disadvantaged populations.
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Aaron Sidford
Associate Professor of Management Science and Engineering and of Computer Science
Current Research and Scholarly InterestsMy research interests lie broadly in the optimization, the theory of computation, and the design and analysis of algorithms. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
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Thierry Tambe
Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science
BioThierry Tambe is an Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science, and the William George and Ida Mary Hoover Faculty Fellow at Stanford University. His research centers on co-designing algorithms and hardware—from high-level models down to custom silicon—to enable efficient execution of AI and data-intensive workloads, with memory efficiency as a central theme. His work has been recognized through an NSF CAREER Award, the inaugural Google ML and Systems Junior Faculty Award, an NVIDIA Graduate PhD Fellowship, an IEEE SSCS Predoctoral Achievement Award, and several distinguished paper awards. Previously, Thierry was a visiting research scientist at NVIDIA and an engineer at Intel. He received a B.S. and M.Eng. from Texas A&M University, and a PhD from Harvard University, all in Electrical Engineering.
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Li-Yang Tan
Associate Professor of Computer Science
Current Research and Scholarly InterestsTheoretical computer science, with an emphasis on complexity theory
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Brian Trippe
Assistant Professor of Statistics and, by courtesy, of Computer Science
BioDr. Brian Trippe is an assistant professor at Stanford in the Department of Statistics, with an affiliation in Stanford Data Science.
In his research, Dr. Trippe develops probabilistic machine learning methods to address challenges in biotechnology and medicine. Recently, his focus has been on generative modeling and inference algorithms for protein engineering.
Before joining Stanford, Dr. Trippe was a postdoctoral fellow at Columbia University in the Department of Statistics, and a visiting researcher at the Institute for Protein Design at the University of Washington. -
Caroline Trippel
Assistant Professor of Computer Science and of Electrical Engineering
BioCaroline Trippel is an Assistant Professor in the Computer Science and Electrical Engineering Departments at Stanford University, where she leads the High Assurance Computer Architectures Lab. Following her PhD, prior to starting at Stanford, Trippel spent nine months as a Research Scientist at Facebook in the FAIR SysML group. Trippel's research fits broadly in the area of computer architecture and focuses on promoting high assurance—correctness, security, and reliability—as a first-order computer architecture design goal. A central theme of her work is leveraging formal methods, especially automated reasoning, techniques to design and verify hardware systems. Trippel research has influenced the design of the RISC-V ISA memory consistency model both via her formal analysis of its draft specification and her subsequent participation in the RISC-V Memory Model Task Group; prompted Intel to update their Software Security Guidance to confirm that two Intel microarchitectures satisfy assumptions made by the Seberus Spectre defense that her lab developed; and produced a novel methodology and tool that synthesized two new variants of the famous Meltdown and Spectre attacks. Trippel's research has been recognized with IEEE Top Picks distinctions, a Sloan Research Fellowship, an NSF CAREER Award, the inaugural Google ML and Systems Junior Faculty Award, the Intel Rising Star Faculty Award, an Intel Outstanding Researcher Award, the 2020 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award, the 2020 CGS/ProQuest® Distinguished Dissertation Award in Mathematics, Physical Sciences, & Engineering, and more.
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Nick Troccoli
Lecturer
BioNick Troccoli is a Lecturer in the Stanford Computer Science Department. He started as a full-time lecturer at Stanford in Fall 2018, after graduating from Stanford in June 2018 with Bachelor's and Master's Degrees in Computer Science. He has taught CS106X, CS107, CS110 and CS111. In 2022, 2024 and 2025 he was named to the Tau Beta Pi Teaching Honor Roll. During his undergraduate career, he specialized in Systems, and during his graduate career he specialized in Artificial Intelligence. He was heavily involved in teaching as both an undergraduate and graduate student; he was an undergraduate Section Leader in the CS 198 Section Leading Program, a graduate CA (Course Assistant) for CS 181, the Head TA for CS 106A and CS 106B, and the summer 2017 instructor for CS 106A. In 2017 he was awarded the Forsythe Teaching Award and the Centennial TA Award for excellence in teaching.
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Jeffrey Ullman
Stanford Warren Ascherman Professor of Engineering , Emeritus
BioJeff Ullman is the Stanford W. Ascherman Professor of Engineering
(Emeritus) in the Department of Computer Science at Stanford and CEO
of Gradiance Corp. He received the B.S. degree from Columbia
University in 1963 and the PhD from Princeton in 1966. Prior to his
appointment at Stanford in 1979, he was a member of the technical
staff of Bell Laboratories from
1966-1969, and on the faculty of Princeton University between
1969 and 1979. From 1990-1994, he was chair of the Stanford Computer
Science Department. Ullman was elected to the National Academy of
Engineering in 1989, the American Academy of Arts and Sciences in
2012, and has held Guggenheim and Einstein Fellowships. He has
received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom
Outstanding Educator Award (1998), the Knuth Prize (2000),
the Sigmod E. F. Codd Innovations award (2006), the IEEE von
Neumann medal (2010), and the NEC C&C Foundation Prize (2017).
He is the author of 16 books, including books
on database systems, compilers, automata theory, and algorithms. -
Gregory Valiant
Associate Professor of Computer Science
Current Research and Scholarly InterestsMy primary research interests lie at the intersection of algorithms, learning, applied probability, and statistics. I am particularly interested in understanding the algorithmic and information theoretic possibilities and limitations for many fundamental information extraction tasks that underly real-world machine learning and data-centric applications.
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Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering and, by courtesy, of Computer Science
BioBenjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His current research focuses on reinforcement learning. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the Lanchester Prize. He was the plenary speaker at the 2019 Allerton Conference on Communications, Control, and Computing. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen. -
Dakuo Wang
Visiting Associate Professor, Computer Science
BioDakuo Wang is a Visiting Associate Professor at Stanford University, and an Associate Professor at Northeastern University, jointly appointed at Khoury College of Computer Sciences and the College of Arts, Media and Design. At Northeastern, Dakuo Wang leads the Northeastern University Human-Centered AI Lab (NEU HAI), and he is also the Founding Director of the AI Application Graduate Program. His research lies at the intersection of human-computer interaction (HCI) and artificial intelligence (AI), with a focus on the exploration, development, and evaluation of human-centered AI (HCAI) systems to achieve human-AI collaboration.
Before joining Northeastern, Dakuo Wang was a Senior Staff Member at IBM Research, Principal Investigator at MIT-IBM Watson AI Lab, and a Visiting Scholar at Stanford Institute for Human-Centered Artificial Intelligence (Stanford HAI). He got his Ph.D. from the University of California Irvine (advisor: Judith Olson and Gary Olson). He has worked as a designer, researcher, and engineer in the U.S., China, and France. He serves in organizing committees, program committees, and editorial boards for a variety of venues, and ACM has recognized him as an ACM Distinguished Speaker. -
Gordon Wetzstein
Associate Professor of Electrical Engineering and, by courtesy, of Computer Science
BioGordon Wetzstein is an Associate Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, artificial intelligence, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, wearable computing, and neural rendering systems. Prof. Wetzstein is a Fellow of Optica and the recipient of numerous awards, including an NSF CAREER Award, an Alfred P. Sloan Fellowship, an ACM SIGGRAPH Significant New Researcher Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), an SPIE Early Career Achievement Award, an Electronic Imaging Scientist of the Year Award, an Alain Fournier Ph.D. Dissertation Award as well as many Best Paper and Demo Awards.
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Steven Whang
Visiting Associate Professor, Computer Science
BioSteven E. Whang is a Visiting Associate Professor at Stanford University, Computer Science (host: Prof. Christopher Ré). He is an Associate Professor with Tenure at KAIST Electrical Engineering and jointly affiliated with the Kim Jaechul Graduate School of AI. His research interests include Data-centric AI and Responsible AI. He is an Associate Editor of IEEE TKDE (2023-2025) and VLDB (2026, 2025), and an Area Chair of ICLR 2025. Previously he was a Research Scientist at Google Research and co-developed the data infrastructure of the TensorFlow Extended (TFX) machine learning platform. Steven earned his PhD in Computer Science in 2012 from Stanford University under Prof. Hector Garcia-Molina. He is a Y-KAST (Young Korean Academy of Science and Technology) member, was a Kwon Oh-Hyun Endowed Chair Professor (2020-2023), and received a Google AI Focused Research Award (2018, the first in Asia). Homepage: https://stevenwhang.com
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Jennifer Widom
Frederick Emmons Terman Dean of the School of Engineering, Fletcher Jones Professor of Computer Science and Professor of Electrical Engineering
BioJennifer Widom is the Frederick Emmons Terman Dean of the School of Engineering and the Fletcher Jones Professor in Computer Science and Electrical Engineering at Stanford University. She served as Computer Science Department Chair from 2009-2014 and School of Engineering Senior Associate Dean from 2014-2016. Jennifer received her Bachelor's degree from the Indiana University Jacobs School of Music in 1982 and her Computer Science Ph.D. from Cornell University in 1987. She was a Research Staff Member at the IBM Almaden Research Center before joining the Stanford faculty in 1993. Her research interests span many aspects of nontraditional data management. She is an ACM Fellow and a member of the National Academy of Engineering and the American Academy of Arts & Sciences; she received a Guggenheim Fellowship in 2000, the ACM SIGMOD Edgar F. Codd Innovations Award in 2007, the ACM-W Athena Lecturer Award in 2015, and the EPFL-WISH Foundation Erna Hamburger Prize in 2018.
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Terry Winograd
Professor of Computer Science, Emeritus
BioProfessor Winograd's focus is on human-computer interaction design and the design of technologies for development. He directs the teaching programs and HCI research in the Stanford Human-Computer Interaction Group, which recently celebrated it's 20th anniversary. He is also a founding faculty member of the Hasso Plattner Institute of Design at Stanford (the "d.school") and on the faculty of the Center on Democracy, Development, and the Rule of Law (CDDRL)
Winograd was a founding member and past president of Computer Professionals for Social Responsibility. He is on a number of journal editorial boards, including Human Computer Interaction, ACM Transactions on Computer Human Interaction, and Informatica. He has advised a number of companies started by his students, including Google. In 2011 he received the ACM SIGCHI Lifetime Research Award. -
Keith Winstein
Associate Professor of Computer Science and, by courtesy, of Electrical Engineering
BioKeith Winstein is an associate professor of computer science and, by courtesy, of electrical engineering at Stanford University. His research group creates new kinds of networked systems by rethinking abstractions around communication, compression, and computing. Some of his group’s research has found broader use, including the Mosh tool, the Puffer video-streaming site, the Lepton compression tool, the Mahimahi network emulators, and the gg lambda-computing framework. He has received the SIGCOMM Rising Star Award, the Sloan Research Fellowship, the NSF CAREER Award, the Usenix NSDI Community Award (2020, 2017), the Usenix ATC Best Paper Award, the Applied Networking Research Prize, the SIGCOMM Doctoral Dissertation Award, and a Sprowls award for best doctoral thesis in computer science at MIT. Winstein previously served as a staff reporter at The Wall Street Journal and worked at Ksplice, a startup company (now part of Oracle) where he was the vice president of product management and business development and also cleaned the bathroom. He did his undergraduate and graduate work at MIT.
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Christina R Wodtke
Lecturer
BioChristina Wodtke is an author, speaker, and lecturer at Stanford with insight into human innovation and high-performing teams. Her resume includes re-design and initial product offerings with LinkedIn, MySpace, Zynga, Yahoo! and others, as well as founding three startups, an online design magazine called Boxes and Arrows, and co-founding the Information Architecture Institute.
Christina uses the power of story to connect with audiences and readers through her worldwide speaking engagements and her Amazon category-bestselling books. Her bestselling book, Radical Focus, tackles the OKR movement and startup culture with an eye to getting the right things done. Her other books include The Team that Managed Itself, Information Architecture: Blueprints for the Web and Pencil Me In (on visual thinking for the workplace.) Christina’s work is personable, insightful, knowledgeable, and engaging. Find out more information (and get your Focus worksheet) at cwodtke.com. -
Jiajun Wu
Assistant Professor of Computer Science and, by courtesy, of Psychology
BioJiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University, working on computer vision, machine learning, robotics, and computational cognitive science. Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. Wu's research has been recognized through the Young Investigator Programs (YIP) by ONR and by AFOSR, the NSF CAREER award, the Okawa research grant, the AI's 10 to Watch by IEEE Intelligent Systems, paper awards and finalists at ICCV, CVPR, SIGGRAPH Asia, ICRA, CoRL, and IROS, dissertation awards from ACM, AAAI, and MIT, the 2020 Samsung AI Researcher of the Year, and faculty research awards from Google, J.P. Morgan, Samsung, Amazon, and Meta.
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Daniel Yamins
Associate Professor of Psychology and of Computer Science
Current Research and Scholarly InterestsOur lab's research lies at intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. It is founded on two mutually reinforcing hypotheses:
H1. By studying how the brain solves computational challenges, we can learn to build better artificial intelligence algorithms.
H2. Through improving artificial intelligence algorithms, we'll discover better models of how the brain works.
We investigate these hypotheses using techniques from computational modeling and artificial intelligence, high-throughput neurophysiology, functional brain imaging, behavioral psychophysics, and large-scale data analysis. -
Diyi Yang
Assistant Professor of Computer Science
BioDiyi Yang is an Assistant Professor in Computer Science at Stanford University. Professor Yang's research interests are Computational Social Science and Natural Language Processing. Her research goal is to understand the social aspects of language and then build socially aware NLP systems to better support human-human and human-computer interaction. Professor Yang received her PhD from the School of Computer Science, Carnegie Mellon University, and her bachelor's degree from Shanghai Jiao Tong University, China. Her work has received multiple best paper nominations or awards at ICWSM, EMNLP, SIGCHI, ACL, and CSCW. She is a recipient of Forbes 30 under 30 in Science, IEEE “AI 10 to Watch”, the Intel Rising Star Faculty Award, Microsoft Research Faculty Fellowship, and NSF CAREER Award.
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James Zou
Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.
