About me

I am currently a Ph.D. candidate at Augmented Health Lab at Dartmouth College, advised by Prof. Temiloluwa Prioleau. Before joining Dartmouth, I received my M.S. in Biomedical Engineering from Duke University, where I worked with Prof. Jessilyn Dunn at the Big Ideas Lab.

My research lies at the intersection of Applied AI/ML, Ubiquitous and Mobile Computing, and Digital Health, with a particular interest in developing intelligent systems that learn from longitudinal, multimodal, and real-world data. My work focuses on leveraging wearable and mobile sensing data to model human behavior and physiology, forecast health-related outcomes, and support personalized health monitoring and decision-making.

My broader research and engineering experience spans time-series foundation models, large language models (LLMs), generative AI, recommendation systems, multimodal learning, and large-scale ML systems. I have worked on benchmarking and adapting foundation models for physiological time-series forecasting, exploring LLM-based architectures for structured sensor data, and developing scalable deep learning pipelines for high-frequency signals such as continuous glucose monitoring (CGM) and photoplethysmography (PPG). More recently, my work has expanded into generative recommendation and retrieval systems, where I explored using large language models to model user behavior and directly generate structured item representations for large-scale recommendation and advertising applications.

Outside of research, I enjoy exploring new technologies, traveling, photography, and discovering good food and coffee. I also enjoy meeting people from different backgrounds and learning about new ideas beyond my own research area.