ORIE Colloquium: John Duchi (Stanford) - Optimality in Optimization
Location
https://cornell.zoom.us/j/546984188
Password: 2020
Description
I will discuss what it means for a method to be optimal in optimization and machine learning. When a method matches a lower bound, does that mean the method is good? How can we develop lower bounds and optimality results that are meaningful? Can theoretical results actually direct progress in what we do? Some of this talk will be speculative, some will cover my and others results, and some will likely be polemic.
Bio:
John Duchi is an assistant professor of statistics and electrical engineering and (by courtesy) computer science at Stanford University. His work spans statistical learning, optimization, information theory, and computation, with a few driving goals. (1) To discover statistical learning procedures that optimally trade between real-world resources—computation, communication, privacy provided to study participants—while maintaining statistical efficiency. (2) To build efficient large-scale optimization methods that address the spectrum of optimization, machine learning, and data analysis problems we face, allowing us to move beyond bespoke solutions to methods that robustly work. (3) To develop tools to assess and guarantee the validity of—and confidence we should have in—machine-learned systems.
He has won several awards and fellowships. His paper awards include the SIAM SIGEST award for "an outstanding paper of general interest" and best papers at the Neural Information Processing Systems conference, the International Conference on Machine Learning, and an INFORMS Applied Probability Society Best Student Paper Award (as advisor). He has also received the Society for Industrial and Applied Mathematics (SIAM) Early Career Prize in Optimization, an Office of Naval Research (ONR) Young Investigator Award, an NSF CAREER award, a Sloan Fellowship in Mathematics, the Okawa Foundation Award, the Association for Computing Machinery (ACM) Doctoral Dissertation Award (honorable mention), and U.C. Berkeley's C.V. Ramamoorthy Distinguished Research Award.