PhD from Texas A&M University
I develop algorithms that help machine learning models adapt to new settings with stronger performance and lower computational costs. My work spans parameter-efficient fine-tuning, physics-informed modeling, and model evaluation, including settings with limited labeled data and noisy signals. I apply these approaches to digital health, computer vision, acoustics, and sensing.
Publications
IEEE International Conference on Bioinformatics and Biomedicine · 2026 · ACCEPTED
Evaluation of computational pathology vision-language foundation model for few-shot histopathology classification with adapting modules
IEEE International Geoscience and Remote Sensing Symposium · 2026 · ACCEPTED
Distribution-aware adaptation of pretrained Transformers for passive and active sonar classification by developing and integrating a learnable histogram module.
IEEE Sensors Journal · 2022
An integrated wearable sensing system, including low-power circuitry, physiological measurements, and machine learning algorithms.