(Ongoing), Presenting at UNC Chapel Hill's Celebration of Undergraduate Research, 2026
Using causal machine learning methods to uncover heterogeneous treatment effects in the OHIE and develop policy targeting algorithms that maximize the impact of Medicaid expansion.
Presented at Argonne National Laboratory's Learning on The Lawn Summer Research Conference, 2023
Utilizing profilers and tracers to analyze I/O and GPU activity in order to optimize large-scale deep learning model training on supercomputers.
Presented at UNC Charlotte's Undergraduate Research Conference, 2023
Leveraging machine/deep learning techniques while engineering new input features to enhance lithium-ion battery health estimation in electric vehicles.
Presented at UNC Charlotte's Summer Research Symposium, 2022
Contributed to the development of a data-driven decision-making system that integrates causal inference and machine learning to improve hospital performance management.