School of Engineering


Showing 631-640 of 710 Results

  • Nick Troccoli

    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, 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.

  • Stephen Tsai

    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.

  • Edison Tse

    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

    Madeleine Udell

    Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering

    Current Research and Scholarly InterestsProfessor Udell develops new techniques to accelerate and automate data science,
    with a focus on large-scale optimization and on data preprocessing,
    and with applications in medical informatics, engineering system design, and automated machine learning.

  • Johan Ugander

    Johan Ugander

    Associate Professor of Management Science and Engineering

    BioProfessor Ugander's research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale data-rich contexts. He is particularly interested in the challenges of causal inference and experimentation in these complex domains. His work commonly falls at the intersections of graph theory, machine learning, statistics, optimization, and algorithm design.

  • Jeffrey Ullman

    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.

  • Camille Utterback

    Camille Utterback

    Associate Professor of Art and Art History and, by courtesy, of Computer Science

    BioCamille Utterback is an internationally acclaimed artist whose interactive installations and reactive sculptures engage participants in a dynamic process of kinesthetic discovery and play. Utterback’s work explores the aesthetic and experiential possibilities of linking computational systems to human movement and gesture in layered and often humorous ways. Her work focuses attention on the continued relevance and richness of the body in our increasingly mediated world.

    Her work has been exhibited at galleries, festivals, and museums internationally, including The Frist Center for Visual Arts, Nashville, TN; The Orange County Museum of Art, Newport Beach, CA; ZERO1 The Art & Technology Network, San Jose, CA; The New Museum of Contemporary Art, The American Museum of the Moving Image, New York; The NTT InterCommunication Center, Tokyo; The Seoul Metropolitan Museum of Art; The Netherlands Institute for Media Art; The Taipei Museum of Contemporary Art; The Center for Contemporary Art, Kiev, Ukraine; and the Ars Electronica Center, Austria. Utterback’s work is in private and public collections including Hewlett Packard, Itaú Cultural Institute in São Paolo, Brazil, and La Caixa Foundation in Barcelona, Spain.

    Awards and honors include a MacArthur Foundation Fellowship (2009), a Transmediale International Media Art Festival Award (2005), a Rockefeller Foundation New Media Fellowship (2002) and a commission from the Whitney Museum for the CODeDOC project on their ArtPort website (2002). Utterback holds a US patent for a video tracking system she developed while working as a research fellow at New York University (2004). Her work has been featured in The New York Times (2010, 2009, 2003, 2002, 2001), Art in America (October, 2004), Wired Magazine (February 2004), ARTnews (2001) and many other publications. It is also included in Thames & Hudson’s World of Art – Digital Art book (2003) by Christiane Paul.

    Recent public commissions include works for the Liberty Mutual Group, the FOR-SITE Foundation, The Sacramento Airport, The City of San Jose, California, The City of Fontana, California, and the City of St. Louis Park, Minnesota. Other commissions include projects for The American Museum of Natural History in New York, The Pittsburgh Children’s Museum, The Manhattan Children’s Museum, Herman Miller, Shiseido Cosmetics, and other private corporations.

    Utterback is currently an Assistant Professor in the Art and Art History Department at Stanford University. She holds a BA in Art from Williams College, and a Masters degree from The Interactive Telecommunications Program at New York University’s Tisch School of the Arts. She currently lives and works in San Francisco.

  • Melissa Valentine

    Melissa Valentine

    Associate Professor of Management Science and Engineering and Senior Fellow at the Stanford Institute for HAI

    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

    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.