School of Engineering
Showing 1,601-1,700 of 1,921 Results
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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, Computer Science
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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Nhi Ngoc Truong
Research Intern, Program-Koyejo, O.
Staff, Program-Koyejo, O.BioNhi is an undergraduate researcher in the Stanford Trustworthy AI Research (STAIR) Lab, advised by Professor Sanmi Koyejo. She studies how we measure the reliability of AI systems, with a focus on the validity of AI benchmarks, meaning whether an evaluation really captures what it claims to and when its results can be trusted. Her current work looks at the assumptions built into widely used language model benchmarks and how to test them more carefully. Her earlier research on language models has appeared at leading natural language processing venues, including co-first-authored work at NAACL 2024 and an ICLR 2024 workshop. Outside of research, she plays in the symphonic band, badminton, and is taking up rock climbing.
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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. -
Emily Wang
Masters Student in Computer Science, admitted Autumn 2026
BioEmily is a Software Engineer within the Wearables team at Meta Reality Labs, with core research interests in artificial intelligence, machine learning, agentic systems and multimodal computing.She holds dual Bachelor’s degrees in Computer Science and Software Engineering from the University of California, Irvine. During her undergraduate career, she conducted research on intelligent systems, computer vision and multi-agent pathfinding algorithms.
While working full-time at Meta, Emily is pursuing a part-time Master of Science in Computer Science through Stanford University’s Honors Cooperative Program (HCP). Her graduate specialization focuses on AI-enabled spatial audio, immersive XR sonics, and edge-adaptive AI systems optimized for wearable hardware.
Her work centers on merging rigorous software engineering principles with applied AI research to build robust, production-ready human-centered systems for voice processing, spatial audio delivery, wearable devices and multimodal interaction. Her long-term objective is to develop high-impact AI products while advancing her research depth in AI system architecture and multimodal intelligence. Emily welcomes academic and industry collaborative opportunities.
