Hi, I’m Jay! Welcome to my personal website.

I’m a senior at UNC Chapel Hill pursuing a B.S. in Economics and Computer Science, with a minor in Statistics.

My academic journey has been shaped by diverse research experiences. I’m currently interested in causal machine learning, particularly its use in treatment effect estimation, heterogeneity analysis, and policy learning from both experimental and observational data.

My ongoing research is focused on using causal machine learning methods to uncover heterogeneous treatment effects in the 2008 Oregon Health Insurance Experiment and develop policy targeting algorithms that maximize the impact of Medicaid expansion.

I aim to pursue a Ph.D. broadly integrating causal inference, econometrics, and machine learning to:

• Develop new methods for understanding complex economic and social systems
• Improve decision-making through robust causal insights
• Advance the theory and applications of causal AI/ML in policy, business, health, and science

I was introduced to this area of work during my first research project, focused on hospital performance management, but came to truly appreciate it during my study abroad at the London School of Economics and Political Science . I took courses in advanced econometrics and machine learning and it was there that I came to understand how the two fields overlap and diverge. I learned how they can be combined not only to model associations, but to learn causal relationships and predict counterfactual outcomes under alternative interventions and policies.

I’ve also conducted research in other domains that incorporated elements of machine learning. I engineered novel features to estimate electric vehicle battery health with neural networks, achieving high out-of-sample accuracy. At Argonne National Laboratory, I analyzed I/O constraints in supercomputers running particle-physics deep learning workloads, identifying key data movement bottlenecks.

Other Interests

Outside of research and academics, I enjoy:

• Learning/playing chess
• Learning/playing game theory optimal (GTO) poker
• Staying active, either in nature or the gym
• Listening to new music
• Traveling

Feel free to reach out using any of the social links on the left side this page!