Stephen P. Boyd

Boyd received an AB degree in mathematics, summa cum laude, from Harvard University in 1980,[3] and a PhD in electrical engineering and computer sciences from the University of California, Berkeley in 1985 under the supervision of Charles A. Desoer, S. Shankar Sastry and Leon Ong Chua.[4][5] In 2006 he was awarded an honorary doctorate from the Royal Institute of Technology in Stockholm, Sweden,[3] and in 2017, from the Université catholique de Louvain in Belgium.[6]

Boyd joined the faculty of Stanford University's Electrical Engineering department in 1985.[3] He regularly teaches undergraduate courses in applied linear algebra and machine learning. During his time at Stanford, he has been recognized with several teaching awards, including the 2016 Walter J. Gores Award for excellence in teaching, the school's highest teaching honor.[7] He was awarded the 2017 IEEE James H. Mulligan Jr. Education Medal, in recognition of his efforts in education in the theory and application of optimization, which has sparked the writing of improved linear algebra and convex optimization textbooks.[8] He has served as director of Stanford's Information Systems Laboratory,[3] and as a visiting professor at universities including City University of Hong Kong, Massachusetts Institute of Technology, New York University, Royal Institute of Technology in Stockholm, and Katholieke Universiteit Leuven in Belgium.[9][10] While at Stanford, he has consulted with numerous Silicon Valley tech companies, and founded one. His groups' CVXGEN software is used in SpaceX's Falcon 9 and Falcon Heavy to guide their autonomous precision landing.[3]

Boyd's primary research interests are convex optimization, especially applications in control, signal processing, machine learning, and finance. His PhD dissertation was on Volterra series descriptions of nonlinear circuits and devices.[11] His primary focus then turned to automatic control systems, where he focused on applying convex optimization, specifically linear matrix inequalities (LMIs), to a variety of control system analysis and synthesis problems.[12]

With Craig Barratt, he authored Linear Controller Design: Limits of Performance in 1991.[13] In 1994, Boyd and Laurent El Ghaoui, Eric Feron, and Ragu Balakrishnan authored the book Linear Matrix Inequalities in System & Control Theory.[14] Around 1999, he and Lieven Vandenberghe developed a PhD-level course and wrote the book Convex Optimization to introduce and apply convex optimization to other fields.[12]

In 2005 he and Michael Grant developed the MATLAB open source software package CVX, which makes it easy to specify and solve convex optimization problems.[15] This work earned them the 2012 Beale-Orchard-Hays Prize for Excellence in Computational Mathematical Programming.[16] In 2012 he and Jacob Mattingley developed CVXGEN, which generates fast custom code for small, quadratic-programming-representable convex optimization problems, using an online interface. With minimal effort, it turns a mathematical problem description into a high-speed solver.[17]

Boyd co-founded and served as chief scientist of analog synthesis and intellectual property provider Barcelona Design, from its 1999 founding until it folded in 2005.[18][19] He serves in an advisory capacity for BlackRock, an investment management corporation;[20] Petuum, a machine learning platform for artificial intelligence;[21] and H2O.ai, open source machine learning platform.[22] He is also a co-inventor on 11 patents.[23] On his personal website, which is visited more than 1.6 million times per year, he makes available papers, books, software, lecture notes and lecture videos.[5]