The ongoing COVID-19 pandemic represents an unprecedented global crisis and serves as a reminder of the social, economic, and health burden of infectious diseases. This panel discussion aims to explore the role that computational and statistical tools can play in supporting policy makers as they formulate and assess policies to control COVID-19. It brings together experts in network science, data-driven modeling, and feedback control theory to discuss how these tools might help to understand the progress of an epidemic, to forecast its future course, to infer properties of a disease, and to choose public policy responses. Moderator Peter Bartlett (Associate Director, Simons Institute, UC Berkeley) will be joined by panelists Klaske van Heusden (Research Associate, University of British Columbia), Madhav Marathe (Professor, Computer Science, University of Virginia), Ankur Moitra (Associate Professor, Mathematics, MIT), Shai Shalev-Shwartz (Professor, Computer Science and Engineering, Hebrew University of Jerusalem), Anil Vullikanti (Professor, Computer Science, University of Virginia), and Bin Yu (Professor, Statistics and EECS, UC Berkeley).
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