For multiple diseases, early detection significantly improves patient outcomes. This motivates considerable investments in population-wide screening programs, such as mammography for breast cancer and low-dose CT for lung cancer. To be effective and economically viable, these programs must balance early detection and overscreening. In this talk, Dr. Yala will discuss approaches to address this challenge by introducing novel methods for cancer risk assessment and personalized screening policy design.

Adam Yala is an assistant professor of Computational Precision Health, Statistics, Electrical Engineering and Computer Science at UC Berkeley and UCSF. His research focuses on developing machine learning methods for personalized medicine and translating them to clinical care. His previous research has focused on two areas: 1) predicting future cancer risk, and 2) designing personalized screening policies. Adam's tools underly multiple prospective trails and his research has been featured in the Washington Post, New York Times, STAT, Boston Globe and Wired. Prof Yala obtained his BS, MEng and PhD in Computer Science from MIT where he was a member of MIT Jameel Clinic and MIT CSAIL.