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 applications in public health, medicine, and human services. 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.

At CMU I direct the Lab for AI and Social Impact (LASI). The group’s work focuses on developing machine learning methods that support robust and equitable decision making. Current domains include maternal and child health, human services, and infectious diseases. Our research is funded by awards from Schmidt Futures, NSF, NIH, and the CDC.

I serve as Chair of the Board of Directors for EAAMO and the associated ACM EAAMO conference. EAAMO is a global research community of over 3000 people which aims to put the perspectives and interests of marginalized groups at the foundation of algorithmic and resource allocation systems.

News

• 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.