About

I am an interdisciplinary researcher working at the intersection of noninvasive sensing, signal processing, and artificial intelligence. My work focuses on translating continuous physiological data from bedside monitors and wearable devices into clinically meaningful insights that enable earlier detection of health changes and more personalized patient care.

I received my Ph.D. in Electrical and Computer Engineering from Cornell University, where I developed a novel wearable radio-frequency sensor for simultaneous respiratory and cardiac monitoring. This system was validated through studies conducted at the Weill Cornell Medical Sleep Center and in healthy participants.

At Biofourmis, a digital health startup, I led research and development efforts in remote patient monitoring. My work included developing a deep-learning model for adult sleep apnea detection using pulse rate and oxygen saturation, designing wearable-based respiratory rate algorithms, and improving SpO₂ alert systems to reduce false alarms in at-home monitoring.

Currently, I am an Instructor in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. In collaboration with clinicians across rheumatology, pediatrics, and cardiology, I apply AI to wearables, clinical and imaging data. My projects include predicting disease activity in rheumatoid arthritis using wearable devices, outcome prediction in transient tachypnea of the newborn, neonatal apnea detection, and echocardiogram-based assessment of diastolic dysfunction.

My long-term goal is to integrate continuous sensing and AI into scalable, real-world clinical solutions that advance precision health monitoring across acute and chronic care settings.

Skills: Physiological signal processing, wearable sensor analytics, machine learning and deep learning, longitudinal time-series modeling, and AI-enabled clinical decision support.