Abstract: Online optimization is a powerful framework in machine learning that has seen numerous applications to problems in distributed systems, robotics, autonomous planning, and sustainability. In my group at Caltech, we began by applying online optimization to ‘right-size’ capacity in data centers a decade ago; and now we have used tools from online optimization to develop algorithms for demand response, energy storage management, video streaming, drone navigation, autonomous driving, and beyond. In this talk, I will highlight both the applications of online optimization and the theoretical progress that has been driven by these applications. Over the past decade, the community has moved from designing algorithms for one-dimensional problems with restrictive assumptions on costs to general results for high-dimensional non-convex problems that highlight the role of constraints, predictions, delay, and more. In the last two years, a connection between online optimization and adversarial control has emerged, and I will highlight how advances in online optimization can lead to advances in the control of linear dynamical systems.

Bio: Adam Wierman is Professor of Computer Science in the Department of Computing and Mathematical Sciences at the California Institute of Technology. He is known for his work on scheduling (computing), heavy tails, green computing, queueing theory, and algorithmic game theory. He was the recipient of an NSF CAREER award in 2009 and the ACM SIGMETRICS Rising Star award in 2011. His work has received "Best Paper" awards at the ACM SIGMETRICS, IEEE INFOCOM, and IFIP Performance conferences, among others. An extension of his work was used in HP's Net-zero Data Center Architecture, which was named a 2013 Computerworld Honours Laureate. His work received the 2014 IEEE William R. Bennet Prize.