Communications and Signal Processing Seminar

Competitive Control via Online Optimization

Adam WiermanProfessorDepartment of Computing and Mathematical Sciences (CMS) at the California Institute of Technology. Director of the Information Science and Technology (IST) initiative

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 fact, 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 a Professor in the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology. He is the director of the Information Science and Technology (IST) initiative and served as Executive Officer (a.k.a. Department Chair) of CMS from 2015-2020. He received his Ph.D., M.Sc. and B.Sc. in Computer Science from Carnegie Mellon University in 2007, 2004, and 2001, respectively, and has been a faculty at Caltech since 2007.

Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He develops new mathematical tools in machine learning, optimization, control, and economics and applies these tools to design new algorithms and markets that can be deployed in data centers, the electricity grid, transportation systems, and beyond. He is best known for his work spearheading the design of algorithms for sustainable data centers, which led to significant industry adoption and was named a Computerworld Honors Laureate.

He is a recipient of multiple awards, including the ACM SIGMETRICS Rising Star award, the IEEE Communications Society William R. Bennett Prize, an NSF Career award, and multiple teaching awards. He is also a coauthor on papers that have received of best paper awards at a wide variety of conferences across computer science, power engineering, and operations research including ACM Sigmetrics, IEEE INFOCOM, IFIP Performance, and IEEE PES.

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