Data Science ARES: Ryan Tibshirani
Join us at the Data Science Applied Research and Education Seminar (ARES) with:
Dr. Ryan Tibshirani
Professor, Departments of Statistics and of Machine Learning
Carnegie Mellon University
Talk Title: Delphi’s COVIDcast Project: Lessons from Building a Digital Ecosystem for Tracking and Forecasting the Pandemic
Abstract: In March 2020, the Delphi group at CMU launched an effort called COVIDcast, which has many parts: 1. unique relationships with partners in tech/healthcare granting us access to data on pandemic activity; 2. infrastructure to build real-time, geographically-detailed COVID-19 indicators from this data; 3. a historical database of all indicators, including revision tracking; 4. a public API serving new indicators daily (with R and Python client support); 5. interactive graphics to display our indicators; 6. forecasting and modeling work building on the indicators. This talk gives a high-level summary, with discussion of some lessons learned.
Speaker Profile: Professor Tibshirani is jointly appointed in the Departments of Statistics and Machine Learning at Carnegie Mellon University. He joined the Statistics faculty at Carnegie Mellon University in 2011, and I joined the Machine Learning faculty in 2013. I did my Ph.D. in Statistics at Stanford University in 2011. My thesis advisor was Jonathan Taylor. Before that, I did my B.S. in Mathematics at Stanford University in 2007.
Prof. Tibshirani’s research interests lie broadly in statistics, machine learning, and optimization. More specifically, high-dimensional statistics, nonparametric estimation, distribution-free inference, continuous optimization, and numerical analysis. His main applied focus at this time is on tracking and forecasting epidemics (previously focused on seasonal flu, and now COVID-19).