PhD, Biomedical Engineering · Columbia University
I work at the intersection of sensor hardware, signal processing, and clinical evidence — taking wearable sensing from prototype through human subjects validation to something people can actually rely on. Currently a postdoctoral fellow at Columbia University.
I finished my PhD in biomedical engineering at Columbia in 2026, where I focused on wearable devices and digital health — designing sensing systems, building the pipelines that turn raw signals into something meaningful, and running the human subjects studies that show whether it actually works. I've stayed on as a postdoctoral fellow, currently working on generative models for synthetic physiological data.
My path there was unusual and I think it's the most useful thing about me. I studied mechanical engineering and EECS as an undergrad, so I'm comfortable moving between mechanical design, embedded systems, and the statistics and machine learning that sit on top of the data. Most wearable projects stall at exactly the seams between those disciplines. I'm used to working across them.
Alongside the research, I spent three years as a fellow at Columbia Technology Ventures evaluating early-stage clinical and digital health technologies — assessing patentability, market viability, and how ready something actually was for human subjects. It taught me to read a technology the way an investor or a partner does, and to write the brief that makes a decision possible.
What I care about is the gap between a device that demos well and one that holds up under real-world conditions with real users. That gap is where most digital health products quietly fail, and closing it is the work I find most interesting.
Feel free to reach out about engagements, collaborations, or questions in either of these areas — they overlap more often than not.
For teams with data they can't yet act on, or a prototype that needs to become a real system.
For groups building a physical device, or preparing to show that one works.
My doctoral and collaborative work on wearable sensing and digital health. A full publication list lives on Google Scholar.
A deep learning method for extracting cardiovascular risk signal from ambulatory blood pressure measurement series. I led the manuscript reframing and the scientific data visualization that communicated how the method worked and where it applied.
So what: Ambulatory monitors already collect this data routinely; the risk information was sitting unused in recordings clinicians were only reading for averages.
npj Biosensing (2025)
Human subjects datasets are small, expensive, and unevenly distributed across the populations that matter. I'm using generative adversarial networks to synthesize physiological signals that augment real datasets, testing whether it makes downstream predictive health models more robust and less brittle outside their training distribution.
So what: If it holds up, teams can validate models against populations they could never afford to recruit at scale — the current binding constraint on digital health ML.
Ongoing — Columbia University
Columbia University
Focus: wearable devices and digital health. Presidential Fellowship.
University of California, Berkeley
Double major across EECS and mechanical engineering.
Consulting inquiries, research collaborations, or just a good question — email or LinkedIn both reach me. I reply to everything legitimate.