TITLE: The Power of Predictions in Online Optimization

ABSTRACT:

Predictions about the future are a crucial part of the decision making process in many real-world online problems.  However, analysis of online algorithms has little to say about how to use predictions, and how properties of prediction errors impact algorithm design.  In this talk, I'll describe recent results exploring the power of predictions in online convex optimization and how properties of prediction noise can impact the structure of optimal online algorithms. I will also briefly highlight applications of these tools to data centers and the smart grid.

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. He is also the director of the Information Science and Technology (IST) initiative at Caltech. He is the founding director of the Rigorous Systems Research Group (RSRG) and co-Director of the Social and Information Sciences Laboratory (SISL). His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS. Additionally, he maintains a popular blog called Rigor + Relevance.