Research Interests

My research is at the intersection of precision medicine, causal inference, and sequential decision-making, with applications in healthcare. I am broadly interested in problems where rigorous statistical thinking can directly inform policy and intervention design.

Current projects include work on adaptive treatment policies using online learning in precision medicine; causal inference with multiple instruments, particularly over-identified instrumental variable settings; and reducing emergency department boarding times through data-driven bed management policies.

Most of these projects are ongoing — I am happy to discuss any of them. Feel free to reach out, and see the Recent News section on the homepage for the latest updates!

Course Projects in UNC

Course projects have been an important part of my research development at UNC. They have allowed me to collaborate, engage seriously with open problems, replicate and extend results from the literature, and explore areas adjacent to my dissertation work.

Semester Course Project Title Materials
Spring 2026 HPM 883 (Causal Machine Learning) Benchmarking Estimators of Local Average Treatment Effect Github Repo
Fall 2025 BIOS 777 (Precision Medicine) Deep RL for Personalized Treatment Recommendation (Main Paper) Report | Slides
Fall 2025 STOR 743 (Reinforcement Learning and MDP) Deep RL for Personalized Treatment Recommendation (Main Paper) Report | Slides
Fall 2025 STOR 664 (Applied Statistics I) NFL Resource Allocations Github Repo
Spring 2026 STOR 672 (Simulations) Possible causes for increased boarding times Report | Slides
Fall 2024 STOR 634 (Probability I) Bayesian NPI of Topic Hierarchies Report

Conferences & Schools Attended

I have made a deliberate effort to attend conferences and workshops across a variety of areas. I find that broad exposure to different methodologies helps me find my footing and think more creatively.