I am an Assistant Professor in the Machine Learning Department at Carnegie Mellon University. My research focuses on AI for equitable, data-driven decision making in high-stakes social settings, integrating methods from machine learning, optimization, and causal inference. Much of my work is motivated by public health applications. I completed my PhD in Computer Science at Harvard University. Before joining CMU as an assistant professor, I was a postdoctoral Schmidt Science Fellow at Harvard School of Public Health and Carnegie Mellon University.
At CMU I direct the Lab for AI and Social Impact (LASI). The group’s work focuses on developing machine learning methods that address challenges in public health. Current domains include infectious diseases and maternal and child health, with past projects in areas such as HIV prevention and tuberculosis treatment. Our research is funded by awards from Schmidt Futures, NSF, and the CDC.
- December 2022: Selected for a Schmidt Futures AI2050 Early Career Award
- August 2022: Invited talk at the KDD 2022 Workshop on Epidemiology meets Data Mining and Knowledge Discovery.
- July 2022: Invited talk in the CPAIOR Master Class on integrating machine learning and discrete optimization.
- January 2022: My dissertation received the IFAAMAS Victor Lesser Distinguished Dissertation Award.
- January 2021: Selected as one of 10 spotlight Rising Stars in Data Science by the University of Chicago.
- September 2020: Paper on inferring between-population differences in COVID-19 dynamics out in PNAS.