I am a joint Machine Learning and Public Policy Management Ph.D. student at the Machine Learning Department of the School of Computer Science and Heinz College of Information Systems and Public Policy at Carnegie Mellon University. I am very fortunate to be advised by Prof. George Chen (Heinz and MLD) and Prof. Jeremy Weiss (National Library of Medicine at NIH). I also work with Prof. Zachary Lipton as my MLD Mentor. During my Ph.D., I have also spent time in conducting research in industry. I spent a summer at Sanofi Inc. working on mRNA-Language Models, collaborating with Saeed Moayedpour, Sven Jager, and Ziv Bar-Joseph. I also spent 2025 working at Google Research, where I was fortunate to be hosted by Vaishnavh Nagarajan and to collaborate with Elan Rosenfeld on understanding how deep sequence models such as Transformers and Mamba tend to memorize geometrically. I am currently a Ph.D. research intern at Microsoft Research: Health Futures, where I work on multimodal reasoning and AI triage systems for contextual healthcare decision-making using longitudinal and conversational patient signals, and am fortunate to collaborate with Mandi Hall.

My research focuses on interpretable representation learning for temporal data with application in healthcare. I am specifically interested in developing machine learning methodology uncovering temporal representations that enhance our understanding of the evolving health status of patients, shedding light on the underlying mechanisms over time. I am also particularly interested in sequence models, especially how the next-token prediction paradigm in large language models shapes both their strengths and limitations, and how these models exhibit implicit forms of memory and reasoning that go beyond simple associative recall. In addition, I am interested in the intersection of machine learning and information systems management, and how we can develop and utilize machine learning tools for high-stakes decision-making scenarios such as those prominent in healthcare management, with a particular emphasis on causal inference and survival analysis.

Research Interests: Representation Learning, Machine Learning for Healthcare, Multimodal Machine Learning, Decision Making, Reinforcement Learning, ML for Temporal Data, Interpretability