School of Engineering
Showing 601-610 of 682 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
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.
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Edison Tse
Associate Professor of Management Science and Engineering, Emeritus
BioProfessor Edison Tse received his BS, MS, and Ph.D. in Electrical Engineering from Massachusetts Institute of Technology. He is the Director of Asia Center of Management Science and Engineering, which has the charter of developing executive training programs for executives in Asian enterprises, conducting research on development of the emerging economy in Asia and establishing research affiliations with Asian enterprises, with a special focus in Greater China: China, Hong Kong, and Taiwan.
In 1973, he received the prestigious Donald Eckman Award from the American Automatic Control Council in recognition of his outstanding contribution in the field of Automatic Control. He had served as an Associate Editor of the IEEE Transactions of Automatic Control, and a co-editor of the Journal of Economic Dynamics and Control, which he co-founded.
Professor Tse has done research in system and control engineering, economic dynamics and control, computer integrated systems to support fishery management policy decisions, management and control of manufacturing enterprise, and industrial competitive analysis and product development. Tse developed a framework for analyzing dynamic competitive strategy that would shape the formation of an ecosystem supporting a value proposition. Within such a framework, he developed dynamic strategies for firms entering an emerging market, latecomers entering a matured market, and firms managing transformation. Using this framework, he developed a new theory on the business transformation of a company and the economic transformation of a developing economy. He applied his theory to explain China’s rapid growth since 1978, changing from a production economy to an innovation economy. His current research is extending the theory to managing product success, managing inflection point disruptions, sustainable growth strategy in a dynamic changing environment, and industries’ strategy responding to geopolitics disruption. Over the years he has made valuable contributions in the field of engineering, economics, and business creation and expansion. He has published over 180 papers on his research activities.
From 2004- 2015, he co-directed various Stanford-China programs on regional industry and enterprise transformation that were attended by high level city officials from various cities in China and high level executives from Chinese enterprises. From 2007-2013, he co-directed a Stanford Financial Engineering Certificate Program in Hong Kong that upgrades the quality of managers and traders in the financial institutions in Hong Kong
He was a co-founder and a Board member of Advanced Decision System (ADS), a technology company with emphasis on AI and advanced decision tools. The company was found in 1979 and later acquired by Booz Allen and Hamilton in 1991. In 1988, Verity was spun off from ADS with AI search engine technology developed in ADS to provide enterprise search software. He was a Board member of Verity representing ADS before Verity went IPO in 1995. From 2007-2010, he was a Board member of KBC Fund Management Co., Ltd. -
Madeleine Udell
Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsProfessor Udell builds the mathematical and computational foundations needed for
scalable, accessible, and responsible data-driven decisionmaking in high-stakes domains, with impact on challenges in healthcare, finance, marketing, operations, and engineering.
She develops new efficient algorithms to accelerate and automate optimization and data science, and new frameworks that empower users to invoke these algorithms and interpret the resulting decisions. -
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. -
Melissa Valentine
Associate Professor of Management Science and Engineering and Senior Fellow at the Stanford Institute for Human-Centered AI
Current Research and Scholarly InterestsAs societies develop and adopt new technologies, they fundamentally change how work is organized. The intertwined relationship between technology and organizing has played out time and again, and scholars predict that new internet and data analytic technologies will spur disruptive transformations to work and organizing.
These changes are already well-documented in the construction of new market arrangements by companies such as Upwork and TaskRabbit, which defined new categories of “gig workers.” Yet less is known about how internet and data analytic technologies are transforming the design of large, complex organizations, which confront and solve much different coordination problems than gig platform companies.
Questions related to the structuring of work in bureaucratic organizations have been explored for over a century in the industrial engineering and organizational design fields. Some of these concepts are now so commonplace as to be taken for granted. Yet there was a time when researchers, workers, managers, and policymakers defined and constructed concepts including jobs, careers, teams, managers, or functions.
My research program argues that some of these fundamental concepts need to be revisited in light of advances in internet and data analytic technologies, which are changing how work is divided and integrated in organizations and broader societies. I study how our prior notions of jobs, teams, departments, and bureaucracy itself are evolving in the age of crowdsourcing, algorithms, and increasing technical specialization. In particular, my research is untangling how data analytic technologies and hyper-specialization shape the division and integration of labor in complex, collaborative production efforts characteristic of organizations. -
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.
