School of Engineering
Showing 5,501-5,600 of 6,507 Results
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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) -
Jakob Thumm
Postdoctoral Scholar, Aeronautics and Astronautics
BioJakob is a postdoctoral scholar in the Department of Aeronautics and Astronautics. His research aims to improve the safety, efficiency, and acceptance of autonomous robots by combining formal methods and machine learning. Jakob focuses on developing algorithms that enable robots to efficiently act in dynamic environments while guaranteeing safety at all times. He is particularly interested in allowing robots to safely work together with humans.
Prior to joining Stanford, Jakob earned his Ph.D. in Computer Engineering from the Technical University of Munich. His doctoral thesis is titled ``Establishing Safe and Preference-Aligned Human-Robot Collaboration in Autonomous Manipulation'' and passed with highest distinctions. Jakob received his M.Sc. and B.Sc. in Mechatronics from the Karlsruhe Institute of Technology, researching the intersection of system modelling and machine learning.
Outside the lab, Jakob is a passionate runner and volunteer at Sutro Stewards, where he maintains hiking trails in the heart of San Francisco. -
Sebastien Tilmans
Student, Civil and Environmental Engineering
BioSebastien is the Executive Director at the Codiga Resource Recovery Center at Stanford University, a test-bed facility dedicated to accelerating the scale-up of innovative resource recovery systems. Prior to joining Stanford, he worked in the Process Engineering group at Oceanside Wastewater Treatment Plant for the San Francisco Public Utilities Commission. He has also designed and implemented several decentralized anaerobic wastewater treatment systems in Panama, and a waterless sanitation service in Haiti. He holds a PhD in Environmental Engineering from Stanford University, and a B.E. in Civil Engineering from Cooper Union. He was a Fulbright scholar, an NDSEG fellow, and an EPA STAR fellow.
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Fouad Tobagi
Professor of Electrical Engineering
BioTobagi works on network control mechanisms for handling multimedia traffic (voice, video and TCP- based applications) and on the performance assessment of networked multimedia applications using user-perceived quality measures. He also investigates the design of wireless networks, including QoS-based media access control and network resource management, as well as network architectures and infrastructures for the support of mobile users, all meeting the requirements of multimedia traffic. He also investigates the design of metropolitan and wide area networks combining optical and electronic networking technologies, including topological design, capacity provisioning, and adaptive routing.
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Alexander Toews
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
Current Research and Scholarly InterestsMagnetic resonance imaging, computational imaging
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Jeffrey B. Tok
Laboratory Director, Chemical Engineering
BioEducation:
The University of Washington, Seattle, WA, B.Sc. (Chemistry & Biochemistry), 1989-1992
The University of Chicago, Chicago, IL, Ph.D. (Bioorganic Chemistry), 1992-1996
Harvard University, Boston, MA, Postdoctoral Research Fellow (Bioorganic Chemistry), 1997-1999
Work Experience:
Assistant Professor, City University of New York, York College and Graduate Center, 1999-2003
Associate Professor, City University of New York, York College and Graduate Center, 2003-2004
Principal Scientist (Indefinite), Lawrence Livermore National Laboratory, 2004-2008
Chief BioScientist, Micropoint Bioscience Inc, 2008-2010
Senior Research Engineer/Scientist, Stanford University, 2010-present
Director, Uytengsu Teaching Center, Shriram Center, 2015-present
Manager, Soft & Hybrid Materials Shared Facility, Stanford Nano Shared Facility, 2010-present
Manager & Instructor, Dept of Chemical Engineering Teaching Lab, 2010-present
Research Activities (via 'Google Scholar'):
https://scholar.google.com/citations?user=hXSGJC0AAAAJ&hl=en&oi=sra -
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. -
Jack Topper
Graduate, Stanford Center for Professional Development
BioJack Topper is a Scientific Software Engineer at NASA’s Community Coordinated Modeling Center (CCMC), where he designs and operates large-scale scientific data and modeling systems supporting the global space-weather research community. His work focuses on automating high-performance computing workflows, building resilient data pipelines, and translating complex scientific models into reliable, user-facing services.
He collaborates closely with domain scientists to bridge research objectives and production-grade software, and has taken on technical leadership responsibilities spanning system architecture, reliability, and user adoption. His interests sit at the intersection of optimization, decision systems, machine learning, and large-scale infrastructure, with an emphasis on how mathematical models and data-driven methods inform real-world operational decisions.
Jack is currently pursuing Stanford’s Data, Models, and Optimization Certificate through the Stanford Center for Professional Development, including coursework in convex optimization and related decision-science foundations. -
Alice Tor
Ph.D. Student in Electrical Engineering, admitted Autumn 2022
BioPhD candidate in Electrical Engineering, advised by Dr. Paul Nuyujukian
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George Toye
Adjunct Professor
BioGeorge Toye, Ph.D., P.E., is adjunct professor in Mechanical Engineering at Stanford University.
While teaching advanced project-based engineering design thinking and STEM-based innovations at the graduate level as part of ME310, he also contributes to research in varied topics in engineering education, and effective globally-distributed team collaborations. As well, he remains active in entrepreneurship and varied advising/consulting work.
George earned his B.S. and M.S. degrees in Mechanical Engineering from U.C. Berkeley, and Ph.D. in Mechanical Engineering with minor in Electrical Engineering from Stanford University.
Since 1983, he has enjoyed volunteering annually to organize regional and state-level Mathcounts competitions to promote mathematics education amongst middle-school aged students. -
Nguyen Dang Khoa Tran
Graduate, Stanford Center for Professional Development
BioA professional practitioner in quantitative finance specializing in portfolio optimization, with a keen interest in machine learning and artificial intelligence
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Simon Treillou
Postdoctoral Scholar, Civil and Environmental Engineering
BioSimon Treillou (he/him) is a postdoctoral researcher at the Baker Coastal Lab at Stanford University, where he studies coastal transport and mixing processes with a focus on wave-driven circulation dynamics. He holds a Master's degree in Applied Mathematics from INSA Toulouse and recently completed his Ph.D. in Coastal Oceanography at the University of Toulouse (France) in the LEGOS lab under the supervision of Patrick Marchesiello. His research uses advanced 3D wave-resolving models to improve the understanding of tracer dispersal in nearshore environments, addressing critical environmental challenges such as contaminant mitigation and ecosystem resilience. Simon's work will integrate numerical modeling, remote sensing, and experimental methods to advance knowledge of coastal physics.
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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 working in the area of computer architecture. Prior to starting at Stanford, Trippel spent nine months as a Research Scientist at Facebook in the FAIR SysML group. Her work focuses on promoting correctness and security as first-order computer systems design metrics (akin to performance and power). A central theme of her work is leveraging formal methods techniques to design and verify hardware systems in order to ensure that they can provide correctness and security guarantees for the applications they intend to support. Additionally, Trippel has been recently exploring the role of architecture in enabling privacy-preserving machine learning, the role of machine learning in hardware systems optimizations, particularly in the context of neural recommendation, and opportunities for improving datacenter and at-scale machine learning reliability.
Trippel's 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. Additionally, her work produced a novel methodology and tool that synthesized two new variants of the now-famous Meltdown and Spectre attacks.
Trippel's research has been recognized with IEEE Top Picks distinctions, the 2020 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award, and the 2020 CGS/ProQuest® Distinguished Dissertation Award in Mathematics, Physical Sciences, & Engineering. She was also awarded an NVIDIA Graduate Fellowship (2017-2018) and selected to attend the 2018 MIT Rising Stars in EECS Workshop. Trippel completed her PhD in Computer Science at Princeton University and her BS in Computer Engineering at Purdue University. -
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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Stephen Tsai
Professor (Research) of Aeronautics and Astronautics, Emeritus
BioProfessor Tsai's research interest is in the development of design methodology of composite materials and structures. As an emerging technology, composite materials offer unique performances for structures that combine light weight with durability. Keys to the successful utilization of composite materials are predictability in performance and cost effective design of anisotropic, laminated structures. Current emphasis is placed on the understanding of failure modes, and computer simulation for design and cost estimation.
