[insert here later] [insert here later] [insert here later], 2025
Using modern causal machine learning methods to uncover Medicaid’s heterogeneous treatment effects on health markers across different groups
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
Building a data-driven system grounded in machine learning and causal inference for hospital performance management.