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

I’m a third-year student at the University of North Carolina at Chapel Hill, pursuing a B.S. in Computer Science and Economics, with a minor in Statistics.

My academic journey has been shaped by diverse research experiences, including:

• Improving hospital performance management via decision models grounded in causal inference
• Machine learning enabled health estimation for lithium-ion batteries in electric vehicles
• Optimizing data movement on supercomputers for deep learning model training at Argonne National Laboratory

Recently, I’ve developed a strong interest in causal machine learning and am currently undertaking an independent study in which I am strengthening and expanding my foundation in causal inference, econometrics, statistics, and machine learning. More specifically, I am exploring machine learning-based methodologies for heterogeneous treatment effect estimation in randomized controlled trials and observational studies. This study is guided by select chapters from the recently released CausalMLBook.

Ultimately, my goal is to pursue a Ph.D. where I can integrate my interests in machine learning, econometrics, and causal inference to:

• Develop advanced methodologies for understanding complex economic and social systems,
• Improve decision-making through data-driven causal insights, and
• Contribute to both the theoretical and applied advancements of causal AI/ML in policy, business, and science.

Other Interests

Outside of research and academics, I enjoy:
🎵 Listening to music
✈️ Traveling
🏋️ Spending time in the gym

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