School of Engineering
Showing 41-60 of 521 Results
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Stephen Boyd
Samsung Professor in the School of Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, and a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
He has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. In 2023, he was given the AACC Richard E. Bellman Control Heritage Award, the highest recognition of professional achievement for U.S. control systems engineers and scientists. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education. In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization." -
Leticia Britos Cavagnaro
Adjunct Professor
BioLeticia Britos Cavagnaro is a scientist-turned-designer working to shape the future of teaching and learning at the Stanford Hasso Plattner Institute of Design (d.school). She holds a PhD from Stanford, at the intersection of Developmental Biology and Computer Science.
Leticia has started and leads several d.school initiatives that support pedagogical innovation globally, such as the Teaching and Learning Studio –an open-enrollment professional development program that has trained over 1000 higher education professors and leaders since 2016– and the Innovative Teaching Scholars program –a collaboration with the Stanford Engineering Center for Global & Online Education to train university educators in Thailand.
She was the founding Deputy Director of the National Center for Engineering Pathways to Innovation (Epicenter), an NSF-funded initiative to foster innovation and entrepreneurship in engineering education across the US that was spearheaded by Stanford's Management Science & Engineering department from 2011 to 2016. With Epicenter as its launchpad, Leticia co-founded, scaled, and spun-off the University Innovation Fellows program, which empowers students globally to be co-designers of their education in collaboration with faculty and leaders at their schools. The program has trained over 3200 Fellows from over 326 universities in 24 countries.
Leticia teaches Capstone Project and Advanced Reflective Practice to graduate students in Stanford’s MS Design program. She also develops tools that leverage emerging technologies like AI to empower learners to be self-directed, action-oriented, and reflective. Riff –the generative AI reflection assistant Leticia developed– is being used by hundreds of educators globally and has supported over 25,000 student reflections in the past year.
Leticia is a thought leader in pedagogical innovation. Her recent book Experiments In Reflection invites us to expand how we see the past and present and to become bold shapers of the future.
She was born in Uruguay and lives in San Francisco with her husband.
Connect with Leticia: https://www.linkedin.com/in/leticiabc/
