BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/New_York X-WR-TIMEZONE:America/New_York BEGIN:VEVENT UID:912@fds.yale.edu DTSTART;TZID=America/New_York:20260325T113000 DTEND;TZID=America/New_York:20260325T130000 DTSTAMP:20260310T184506Z URL:https://fds.yale.edu/events/fds-colloquium-cliff-stein-columbia/ SUMMARY:FDS Colloquium: Cliff Stein (Columbia)\, "Energy-Efficient Scheduli ng with Predictions" DESCRIPTION:\nAbstract: In energy-efficient scheduling\, the operating syst em controls the speed at which a machine is processing jobs with the dual objectives of minimizing energy consumption and optimizing the quality of service cost of the resulting schedule. Since machine-learned predictions about future requests can often be learned from historical data\, a recent line of work on learning-augmented algorithms aims to achieve improved pe rformance guarantees by leveraging predictions.   In this talk\, we fir st consider a general setting for energy-efficient scheduling and provide a flexible learning-augmented algorithmic framework that takes as input an offline and an online algorithm for the desired energy-efficient scheduli ng problem. We show that\, when the prediction error is small\, this frame work gives improved competitive ratios for many different energy-efficient scheduling problems\, including energy minimization with deadlines\, whil e also maintaining a bounded competitive ratio regardless of the predictio n error. Then\, we empirically demonstrate that this framework achieves an improved performance on real and synthetic datasets.\n\n\n\nThese results \, while being quite general\, require extensive predictions about the inp ut.  We next consider whether similar results are possible for the case when you have much lighter-weight predictions: namely predictions about th e speed at which the machine will run.  We show how\, for the problem of scheduling to minimize energy while meeting deadlines\, we are able to ac hieve similar guarantees using the speed predictions.\n\n\n\nThis represen ts joint work with Eric Balkanski\, Jingwei Li\, Ting-Ting Ou\, Noemie Pre ivier\, Hao-Ting Wei\, and Cherlin Zhu.\n\n\n\nSpeaker Bio: Clifford Stein is a Professor of \;IEOR \;and of \;Computer Science \;at  \;Columbia University. He is also the Associate Director for Research in the \;Data Science Institute. \;From 2008-2013\, he was chair of the IEOR department. Prior to joining Columbia\, he spent 9 years as an Assistant and Associate Professor in the \;Dartmouth College Departme nt of Computer Science.\n\n\n\nHis research interests include the design a nd analysis of algorithms\, combinatorial optimization\, operations resear ch\, network algorithms\, scheduling\, algorithm engineering and computati onal biology. Professor Stein has published many influential papers in the leading conferences and journals in his field\, and has occupied a variet y of editorial positions including the journals ACM Transactions on Algori thms\, Mathematical Programming\, Journal of Algorithms\, SIAM Journal on Discrete Mathematics and Operations Research Letters. His work has been su pported by the National Science Foundation and Sloan Foundation. He is a F ellow of the Association for Computing Machinery (ACM). He is the winner o f several prestigious awards including an NSF Career Award\, an Alfred Slo an Research Fellowship and the Karen Wetterhahn Award for Distinguished Cr eative or Scholarly Achievement. He is also the co-author of the two textb ooks. \;Introduction to Algorithms\, \;with T. Cormen\, C. Leisers on and R. Rivest is currently the best-selling textbook in algorithms and has sold over half a million copies and been translated into 15 languages.  \;Discrete Math for Computer Scientists \;\, with Ken Bogart and Scot Drysdale\, is a text book which covers discrete math at an undergradu ate level.\n CATEGORIES:Fellows Events,FDS Events,Colloquium LOCATION:Yale Institute for Foundations of Data Science & Webcast\, 219 Pro spect Street\, 13th Floor\, New Haven\, CT\, 06511\, United States X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=219 Prospect Street\, 13th Floor\, New Haven\, CT\, 06511\, United States;X-APPLE-RADIUS=100;X-TITLE= Yale Institute for Foundations of Data Science & Webcast:geo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR