January 16, 2020 - 4:00pm
SIEPR, Koret-Taube Conference Center
366 Galvez Street, Stanford, CA 94305
366 Galvez Street, Stanford, CA 94305
This event is only for current Stanford staff, faculty, and students.
Mykel Kochenderfer, Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University
Mykel is Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. Of particular interest are systems for air traffic control, unmanned aircraft, and automated driving where decisions must be made in uncertain, dynamic environments while maintaining safety and efficiency. Research at SISL focuses on efficient computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations.
Prior to joining the faculty in 2013, he was at MIT Lincoln Laboratory where he worked on airspace modeling and aircraft collision avoidance. He received his Ph.D. from the University of Edinburgh in 2006 where he studied at the Institute of Perception, Action and Behaviour in the School of Informatics. He received B.S. and M.S. degrees in computer science from Stanford University in 2003. Prof. Kochenderfer is the director SAIL-Toyota Center for AI Research and a co-director of the Center for AI Safety. He is affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), the Human-Centered AI (HAI) Institute, the Symbolic Systems Program, the Bio-X Institute, Wu Tsai Neurosciences Institute, and the Center for Automotive Research at Stanford (CARS). In 2017, he was awarded the DARPA Young Faculty Award. He is an associate editor of the Journal of Artificial Intelligence Research and the Journal of Aerospace Information Systems. He is an author of the textbooks Decision Making under Uncertainty: Theory and Application (MIT Press, 2015) and Algorithms for Optimization (MIT Press, 2019). He is a third-generation pilot.
Bryan Casey, Legal Fellow at the Center for Automotive Research at Stanford University
Bryan Casey is a Legal Fellow at the Center for Automotive Research at Stanford, a Lecturer at Stanford Law School, and an affiliate scholar at the Stanford Machine Leaning Group, CodeX: The Center for Legal Informatics, and the Transatlantic Technology Law Forum. His research covers a broad range of issues at the intersection of law and emerging artificial intelligence technologies—particularly those involving transportation systems. He was written extensively on the legal implications of machine decision making, algorithmic explanability, and the role of lawyers as gatekeepers overseeing the deployment of AI-embedded products.
Bryan’s scholarship has appeared in Northwestern University Law Review, Berkeley Technology Law Journal, and Stanford Law Review Online, among other journals. He also regularly comments in media outlets including CNN, Wired Magazine, Futurism, and The Stanford Lawyer. His recent work focuses on the competing roles of legality, morality, and profit-maximization in commercial AI systems with significant social impacts. And his 2018-2019 course offerings at Stanford Law School include The Future of Algorithms and Lawyering for Innovation: Artificial Intelligence.
Clark Barrett, Associate Professor (Research) of Computer Science, Stanford University
Clark Barrett joined Stanford University as an Associate Professor (Research) of Computer Science in September 2016. Before that, he was an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. His expertise is in constraint solving and its applications to system verification and security. His PhD dissertation introduced a novel approach to constraint solving now known as Satisfiability Modulo Theories (SMT). Today, he is recognized as one of the world's experts in the development and application of SMT techniques. He was also an early pioneer in the development of formal hardware verification: at Intel, he collaborated on a novel theorem prover used to verify key microprocessor properties; and at 0-in Design Automation (now part of Mentor Graphics), he helped build one of the first industrially successful assertion-based verification tool-sets for hardware. He is an ACM Distinguished Scientist.
Chris Gerdes, Professor of Mechanical Engineering, Director of the Center for Automotive Research (CARS), and Director of the Revs Program, Stanford University
Chris studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, instrumenting historic race cars or trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle, and Shelley, an Audi TT-S capable of turning a competitive lap time around the track without a human driver. Professor Gerdes and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.