Research Bio

Sandya Subramanian is a computational scientist-engineer whose research integrates statistics/AI, engineering, neuroscience, and physiology to improve patient outcomes through personalized insight discovery. Her work develops algorithms and machine learning models to analyze physiological signals and clinical data for precision diagnosis and treatment ("physiology-informed AI"). She focuses on developing both hardware and software tools to capture physiological signals from patients in complex clinical settings and at home continuously, while also extracting clinically relevant insights from multi-organ data across the body through deep individual phenotyping. Her lab works with a wide range of signals from the body, including brain signals, heartbeat dynamics, sweat gland activity, and stomach muscle activity. Sandya is an Assistant Professor in the Division of Computational Precision Health, a joint program between UC Berkeley and UCSF. She is also affiliated with the Berkeley AI Research Lab (BAIR) and the UCSF Department of Neurosurgery. 

Research Expertise and Interest

translational medicine, computational neuroscience, physiology, statistics, mathematical modeling