QB3-UCSC Graduate Fellowship for Innovators
I’m developing techniques to transform everyday Wi-Fi signals into clinical-grade health monitoring tools as part of the PulseFi project in the INRG lab. My research focuses on extracting physiological signals like heart rate and breathing rate from wireless Channel State Information (CSI), allowing non-contact vital sign monitoring with accuracy comparable to medical devices. I’ve designed algorithms that filter noise from raw CSI data and am now building multi-person sensing models that generalize across diverse environments, from homes to clinical settings. This work bridges wireless communication, machine learning, and biomedical signal processing, driven by the need for accessible, privacy-preserving health technologies. My broader research interests span wireless systems, embedded sensing, and computational health, with the goal of translating RF signal physics into practical tools that improve health monitoring for individuals and communities.