Online Optimization and Energy
Online optimization is a powerful framework in machine learning that has seen 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 a decade, we have moved from designing algorithms for one-dimensional problems with restrictive assumptions on costs to general results for high-dimensional problems that highlight the role of constraints, predictions, multi-timescale control, and more.
Bio: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he currently serves as Executive Officer. His research interests center around learning, optimization, and economics in networked systems. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been a co-author on papers that received best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance, IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.