Event Date
Professor Stark Draper
Department of Electrical and Computer Engineering, University of Toronto
Abstract:
The availability of big data and the use of these data in training artificial intelligence (AI) systems is changing the way companies do business in a wide swath of industries, from finance to entertainment to drug discovery. These advances rely on a computing, networking, and data storage infrastructure, the growing capabilities of which are key to realizing the promise of AI. However, as data sets and processing requirements scale up massively, classic single-processor computing paradigms cannot keep up. There has therefore been a renaissance in understanding how to bring large-scale parallelization to bear on algorithmic design for learning-specific workloads. In this talk we describe some themes of research that address how to parallelize tasks when the underlying computing fabric is non-ideal. In particular, we consider unpredictable delays in the completion of allocated tasks by individual worker nodes (often termed "stragglers") and in the communication amongst the nodes. We discuss the wider literature and focus on a particular “anytime” minibatch approach to distributed optimization (ICLR’19, JSAIT’21, TSIPN’21,’22,T-ServicesComp’24) that we have developed in our group. Given time we will also mention some of our work in coded computing for large-scale matrix-matrix multiplication (ISIT’18, TIT’21, JSAIT’22).
Bio:
Professor Stark Draper is a Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Toronto (UofT). He received the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT), and the B.S. and B.A. degrees in Electrical Engineering and History, respectively, from Stanford University.
Professor Draper has received the NSF CAREER grant, the MERL 2010 President's Award, the NSERC Discovery Award, teaching awards from the University of Toronto, from UW-Madison, and from MIT, an Intel Graduate Fellowship, Stanford's Frederick E. Terman Engineering Scholastic Award, and a Fulbright Fellowship.
Professor Draper currently serves as the Vice-Dean Research of the Faculty of Applied Science and Engineering at the UofT. He is Senior Past President of the IEEE Information Theory Society (ITSoc), having served as President in 2024, and on the ITSoc Board and in various roles since 2016 including chairing the Diversity and Inclusion Committee, the Schools Committee, and as Secretary to the BoG. He was the first chair of the Machine Intelligence major in the UofT Department of Engineering Science. He served as a member of the UofT Governing Council from 2020 to 2022.
Professor Draper’s research interests and activities include information theory, coding theory, optimization, learning, security, and applications in computing, communications, and astronomy.