Data for Good seminars address societal challenges and bring humanistic perspectives to science and technology.

Guest Speaker

Peter Frazier, Associate Professor, School of Operations Research and Information Engineering, Cornell University

September 23, 2020 (11:00 AM – 12:00 PM ET) – Online Event
WATCH RECORDING

Hosted By

DSI Financial and Business Analytics Center

About the Seminar

Reopening Cornell During the COVID-19 Pandemic

Abstract: Colleges and universities around the world faced gut-wrenching decisions this summer: whether to reopen for the fall semester and how to control COVID-19 in campus populations if they did. Data science was a fundamental part of these decisions at Cornell. First, models developed by Cornell’s COVID-19 Mathematical Modeling Team were used to design the testing interventions that are a cornerstone of Cornell’s COVID-19 control strategy: targeted asymptomatic screening that tests all undergraduates twice per week and an adaptive testing program that goes beyond traditional contact tracing to test the full social circle of positive cases. Second, these same models were the basis for Cornell’s decision to reopen for a residential fall semester. They showed that reopening with aggressive mandatory testing was surprisingly less risky than virtual instruction. Data suggested that thousands of students would return to Ithaca whether residential instruction was offered or not, and a weaker ability to enforce mandatory testing for these students risked being unable to control clusters in that population. This talk will give intuition for the main factors that influence outcomes and appropriate test designs, explain practical tools and approaches that supported this work, and articulate the main uncertainties and challenges we confronted.

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 Mathematical 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.