I am a tenure-track assistant professor of computer science at Emory University. My research focuses on developing and applying artificial intelligence methods to enhance decision making in healthcare, with a particular emphasis on reinforcement learning. Inspired by specific real-world use cases, my work identifies technical challenges and proposes novel solutions that are broadly relevant to fundamental AI/ML research. My past contributions span various technical areas including reinforcement learning (NeurIPS'23, ICML'23 workshops, NeurIPS'22 oral, NeurIPS'22 workshop, MLHC'21, ICML'20), time-series and sequence data modeling (MLHC'19), dataset bias (HealthAffairs'21), as well as translating AI solutions to advance precision medicine (IDWeek'24, AJS'21, JAMIA'20, JCO-CCI'20), with some work specifically addressing the COVID-19 pandemic (BMJ'22, AnnalsATS'20). I hope to apply my knowledge and expertise to interdisciplinary problems in healthcare and beyond.

I served as a main organizer for the ML4H Symposium in 2022 and 2023, and as an area chair for CHIL 2024. I also regularly review for top-tier ML venues, including NeurIPS, ICML, ICLR, AAAI, AISTATS, KDD, MLHC, CHIL, TMLR, TKDD.

I am a "Triple Wolverine" - having done my bachelor's, master's, and PhD degrees all at University of Michigan. During my PhD, I was a member of the Machine Learning for Data-Driven Decisions (MLD3) research group led by Prof. Jenna Wiens, under the Michigan AI Lab. I also collaborated with Prof. Maggie Makar at UMich, Prof. Sonali Parbhoo at Imperial, and Prof. Finale Doshi-Velez at Harvard.