Ezra's Round Table / Systems Seminar: Peter Frazier (Cornell ORIE) - Fighting COVID-19 at Cornell
Mathematical modeling is a critical piece of the fight against COVID-19 at Cornell. First, mathematical modeling played a key role in the decision to reopen: models showed that reopening with aggressive mandatory testing is less risky than virtual instruction with a weak testing mandate across a wide range of parameters. Second, it helped design the testing interventions that are a cornerstone of Cornell’s COVID-19 control strategy. This includes our asymptomatic screening program that is nearly unparalleled in its breadth and frequency. It also includes our unique-to-Cornell adaptive testing program that blends traditional contact tracing with asymptomatic screening to race ahead and encircle the virus. This talk aims to give intuition for the main factors that influence outcomes and appropriate test designs, explain practical tools and approaches that we found useful and believe generalize to other practice-oriented modeling work, and articulate the main uncertainties and challenges we confronted in doing this work. This is joint work with the other members of the Cornell COVID-19 Modeling Team: Massey Cashore, Ning Duan, Alyf Janmohamed, Jiayue Wan, Yujia Zhang, Shane Henderson, and David Shmoys. Bio: Peter Frazier is an Associate Professor in Cornell ORIE and a Staff Data Scientist at Uber. He received a Ph.D. in Operations Research and Financial Engineering from Princeton University in 2009. Since spring 2020, he has led Cornell's COVID-19 Modeling Team. His academic research during more ordinary times is on the optimal collection of information, including Bayesian optimization, incentive design for social learning, and multi-armed bandits, with applications in applications in e-commerce, the sharing economy, and materials design. At Uber, he managed UberPool's data science group and currently helps to design Uber's pricing and incentive systems.