I am a Research Fellow (Computational Science) at the Regulation, Evaluation, and Governance Lab (RegLab), Stanford Law School, where I work on developing and validating computational methods to study racial disparities. I graduated with Honors from Master of Science in Computational Analysis and Public Policy program in 2022, a joint degree between the Harris School of Public Policy and Department of Computer Science at the University of Chicago. I focus on international development policy, public health, social inequality, and machine learning methods. Prior to that, I worked as a research consultant at the World Bank for DIME and SSI where I applied spatial data science methods to support field teams in geocoding in Delhi, India and identifying vulnerable populated regions in Ukraine. I received a B.S. with Distinction from the University of Michigan - Ann Arbor in Statistics and Political Science (with a minor in Economics) in 2020, where I applied statistical methods to examine factors associated with experience of intimate partner violence.

Research Interest

computational health economics, health policy, public health, machine learning

Teaching Interests

As a graduate student at the University of Chicago, I have worked as a teaching assistant at Harris School of Public Policy. You can read more about the exact courses and descriptions under Teaching.