[ https://princeton.zoom.us/webinar/register/WN_lEqS6LEqQueku0tG72vBIg ] [ https://princeton.zoom.us/webinar/register/WN_lEqS6LEqQueku0tG72vBIg ] Register here: [ https://princeton.zoom.us/webinar/register/WN_lEqS6LEqQueku0tG72vBIg | https://princeton.zoom.us/webinar/register/WN_lEqS6LEqQueku0tG72vBIg ] ABSTRACT Online optimization is a powerful framework in machine learning that has numerous applications to problems in energy and sustainability. In my group at Caltech, we began by applying online optimization to ‘right-size’ capacity in data centers nearly a decade ago; and by now, tools from online optimization have been applied to develop algorithms for geographical load balancing among data centers, demand response, generation planning, energy storage management, and beyond. In this talk, I will highlight both the applications of online optimization and the theoretical progress that has been driven by applications in energy and sustainability. Over the last decade, we have 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, multi-timescale control, and more. 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 member at Caltech since 2007. Wierman’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 for which he 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 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.