Machine Learning and Science Forum — Machine-Learned Epidemiology

ML&Sci Forum

October 12, 2020
11:00am to 12:00pm
Virtual Participation

Machine Learning and Science Forum 
Date: Monday, October 12, 2020
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link 

Machine-Learned Epidemiology

Adam Sadilek, Google Research
Abstract: Work in computational epidemiology to date has been limited by coarseness and lack of timeliness of observational data. Most existing models are based on hand-curated statistics that are often delayed, expensive to collect, and cover only limited jurisdictions. Our goal is to lift the state of the art in epidemiology to a new qualitative state, where real-time health predictions become feasible and actionable. We do this at scale by applying federated machine learning and secure aggregation to online data to infer what likely contributed to the contagion. In this talk, I will sample current projects at Google focusing on privacy-first epidemiology research and recent publications (e.g.,,,,,

The Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by UC Berkeley Physics Professor and BIDS Faculty Affiliate Uros Seljak, these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact berkeleymlforum@gmail.comAll interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. Full details about this meeting are posted here:


Adam Sadilek

Google Research

Adam Sadilek focuses on large-scale machine learning applied to health and ecology at Google Research. Before that, he worked on speech understanding at Google[x]. Prior to joining Google, Adam was a co-founder of, a machine learning startup providing automated text understanding.