My current research interests is on reinforcement learning, language agents & reasoning, sampling techniques, and contextual bandits. I am currently building Machine Learning algorithms @ Apple, where I am building environments, benchmarks, and evals to train RL agents at scale. To this end, I believe that research in scalable supervision is crucial to train next-generation models as they grow in complexity and as their applications extend towards domains where we can no longer define rewards easily.

I received my doctorate in Electrical Engineering and Computer Science at Texas A&M University under the supervision of Dr. Xiaoning Qian. During my studies, I researched uncertainty quantification and variational inference in machine learning, with applications toward continual learning, anomaly detection, model compression & energy-efficiency in areas such as computer vision, recommender systems, time-series prediction, and healthcare monitoring. My work has been published in conferences such as ICML, AAAI, AISTATS, and NeurIPS, as well as healthcare journals such as JHIR and Surgical Infections.

Download my resumé.