I am an Assistant Professor in the Machine Learning Department at Carnegie Mellon University. I work on safe and trustworthy AI. Most of my research falls into two categories. First, I develop scientific foundations for AI systems that support legible, valid, and robust decision making. Second, I conduct applied work with partners in social, policy, and healthcare settings to deploy AI and rigorously evaluate its impacts. At CMU, I direct the Lab for AI and Social Impact. Our research has been funded by Schmidt Sciences, NSF, NIH, CDC, the Engler Family Foundation, and ARO. I completed my PhD in Computer Science at Harvard University. Before joining CMU, I was a postdoctoral Schmidt Science Fellow at the Harvard School of Public Health. I serve as Chair of the Board of Directors for EAAMO and the associated ACM EAAMO conference.

News

• July 2025: Paper on “Learning treatment effects while treating those in need” received the exemplary paper award for the AI track at EC 2025.
• February 2024: My dissertation received the honorable mention for the AAAI/ACM SIGAI Best Dissertation Award.
• September 2023: I will serve as co-PI of a CDC Center for Innovation at CMU.
• July 2023: Gave an invited tutorial at the International Conference on Stochastic Programming
• 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.