Ph.D. Candidate, Computer Engineering
Watson Research Lab,
University of Virginia
Advised by Prof. Amanda Watson
· Charlottesville, VA
I'm a Ph.D. candidate in Computer Engineering at the University of Virginia, working in the Watson Research Lab with Prof. Amanda Watson.
My research is on machine learning for healthcare — building wearable monitoring systems that detect biomarkers such as glucose and opioids in real time from multi-wavelength optical and time-series sensor data. I'm also interested in ubiquitous computing and multimodal deep learning. Before UVA, I earned an M.S. in Computer Science from Duke University and a B.S. in Information Systems from the George Washington University.
OptiLact: A Wearable Broad-band Optical Device for Continuous Lactate Monitoring during Exercise Under review
In submission, 2026
OpenSpectro: An Open-Source Spectroscopic Profiling Platform EMBC 2025
47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2025
A Multi-Wavelength Optical Sensing Framework for Calibration-Free Wearable Blood Pressure Monitoring ICASSP 2025
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025
A wearable broad-band optical spectroscopy system for noninvasive lactate estimation during exercise. It captures broadband optical signals across lactate-sensitive bands and applies a two-stage hierarchical regression — a PLS model for the per-session trend and an ElasticNet model for the absolute level. Validated in vitro and in an in-vivo cycling study (11 participants, VO₂max protocols), reaching R² ≈ 0.62. Paper under review.
The full hardware-and-software stack for the Lumos multi-wavelength wearable spectroscopy device: Arduino firmware across board revisions (9–11 LEDs, up to 940 nm), Python analysis tooling, and a companion Swift iOS app.
An open-source Python package for storing, managing, analyzing, and visualizing multi-wavelength PPG measurements from the Lumos wearable. Each Adub object holds one subject's measurement, with dataset storage, export, and quick-view utilities.