I'm a sophomore at Los Altos High School in Los Altos, CA. I tend to notice when something is not working, and I keep going until I understand why. That is roughly how CivicPulse started.
I kept walking past cracked sidewalks and broken streetlights around my neighborhood that nobody seemed to fix. It turned out there was no simple way for residents to flag those problems to the city, so I built one.
Outside of software, I mentor about 60 students a year in Math Kangaroo prep, compete on the VEX and FRC robotics teams, and run a STEM Research Club I started at LAHS to help students find research opportunities at nearby universities. I also have 200+ volunteer hours across the library, FCSN, and STEM outreach.
Receiving a Certificate of Appreciation for CivicPulse from the Rotary Club of Mountain View.
CivicPulse is a platform where Los Altos residents can report local infrastructure problems, upvote what matters most to them, and track whether anything gets resolved. Residents drop a pin, attach a photo, and submit a report in under a minute. The app has a live map of all active reports, community upvoting so the most urgent issues surface first, push notifications, and an admin dashboard for city staff. It was covered by the Los Altos Town Crier in April 2026 and is in active use with LAMVCF and the Rotary Club. The iOS app is live on the App Store.
Co-authored with Caleb Suh. Science fair project, in progress.
Drug discovery for neglected tropical diseases is underfunded and computationally expensive. Screening a billion compounds on a single processor core would take 475 years. Caleb and I are building a surrogate modeling pipeline that uses a Multi-Output Gaussian Process trained on molecular fingerprints to predict ADMET properties, meaning Absorption, Distribution, Metabolism, Excretion, and Toxicity, from a molecule's SMILES string alone. Rather than collapsing drug-likeness into a single score, the model treats human safety, absorption, and synthesizability as conflicting objectives on a Pareto front, and uses Expected Hypervolume Improvement to choose the next molecule to evaluate. The goal is to let underfunded NTD researchers skip expensive wet lab screening and go straight to a ranked list of promising candidates. We are validating the approach by simulating rediscovery of a known drug at a fraction of the documented cost and time.
