School of Engineering
Showing 5,601-5,650 of 6,548 Results
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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, 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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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.
