Computer Science
Showing 1-81 of 81 Results
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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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Tristan Thrush
Ph.D. Student in Computer Science, admitted Autumn 2023
BioI'm a Computer Science PhD student at Stanford in the NLP group and AI lab, supervised by Tatsunori Hashimoto and Christopher Potts. Previously, I was a founding member of the technical staff at Contextual AI (a startup working on retrieval augmented generation). Before that, I was a research engineer at Hugging Face. Before that, I was a research associate at Facebook AI Research, supervised by Douwe Kiela and then Adina Williams. And before that, I was a research associate at MIT Brain and Cognitive Sciences, supervised by Roger Levy. I Received my MEng in computer science with a concentration in artificial intelligence under Patrick Winston at the MIT Computer Science and Artificial Intelligence Lab. I received my BS also at MIT in computer science, with a minor in linguistics and a minor in math. While I was an undergrad, I did research with the Perception Systems Group at NASA's Jet Propulsion Lab.
I'm interested in AI. Specifically: natural language processing, computer vision, high-dimensional statistics, and data-centric AI methods. I have done several large-scale projects with a focus on the data side, which is so intertwined with the model side that it is sometimes hard to tell where one ends and the other begins.
Here are three of my favorite papers:
Perplexity Correlations: https://arxiv.org/abs/2409.05816
(This one has some fun math and is useful for pretraining data selection)
Multimodal Evaluation: https://arxiv.org/abs/2204.03162
(This one poses a still open challenge for word-order understanding in vision-language models)
Rover Relocalization for Mars Sample Return: https://ieeexplore.ieee.org/abstract/document/9381709
(There is nothing cooler than robots in space) -
Alberto Tono
Ph.D. Student in Civil and Environmental Engineering, admitted Autumn 2021
Ph.D. Minor, Computer Science
Grad RA student-Hourly, Institute for Human-Centered Artificial Intelligence (HAI)BioTono Alberto is a current PhD Student at Stanford under the supervision of Kumagai Professor: Martin Fischer. He is currently exploring ways in which the Convergence between Digital and Humanities can facilitate cross-pollination between different industries within an Ethical Framework focused on augmenting human intelligence.
He served as the Research and Computational Design Leader in Architectural and Engineering organizations, receiving the O1-visa for outstanding abilities with both HOK and HDR. Tono obtained his Masters in Building Engineering - Architecture from the University of Padua and the Harbin Institute of Technology under the supervision of Andrea Giordano, Carlo Zanchetta and Paolo Borin. He has been working in the computational design and deep learning space since 2014. Furthermore, he is improving Building Information Modeling and Virtual Design and Construction (BIM/VDC) workflows within a statistical framework to optimize the sustainability impact of these processes. Hence, Tono is LEED AP certified. He is an international multi-award-winning “hacker” and speaker, and his work within Architecture and Artificial Intelligence brought him to companies in China, the Netherlands, Italy, and California. Thanks to his multidisciplinary approach he worked as Data Scientist and Geometric Deep Learning Researcher at a Physna/Thangs helping to raise over 80 Milion while working on 3D Search and Monocular 3D Shape Retrieval problems.
Currently is focusing on better methodologies for Generative Building Design, centered on capturing design knowledge from the primordial and universal act of Sketching. -
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.
