Shortening the distance between thinking, building, testing, and learning.
I study cognitive science and data science at UC Berkeley because good products start with understanding people and end with data-driven decisions. I've shipped an AI diabetes platform backed by 10K+ glucose readings for my twin sister's family, a marketplace that kept 127 items out of landfills, and a lyric engine that makes non-English music actually make sense.
I care a lot about accessibility and using data to surface insights for people who usually get left out. That's what led me to building gesture-based games for kids with motor disabilities at a clinic in Mumbai, and what drives most of what I make.
Problem: US colleges generate 12.1M tons of move-out waste every year. Students throw away furniture that incoming students buy brand new three months later.
What I built: A hyperlocal peer-to-peer marketplace. List it, match it, hand it off within the 72-hour move-out window.
Problem: Families facing a new Type 1 diagnosis juggle glucose readings, carb math, and emergencies across disconnected tools.
What I built: An AI glucose risk engine that turns CGM noise into plain-English caregiver alerts. Started after my twin sister's diagnosis.
Problem: 128M people watched Bad Bunny's Super Bowl halftime. Google Translate gave them "Uncle asked me" for "Titi Me Preguntó." The music hits. The meaning doesn't.
What I built: A Spotify-connected lyric sync engine with contextual translations. Poetry stays poetry. Slang stays slang.
Problem: Kids with motor and cognitive disabilities were locked out of learning games built for able-bodied play.
What I built: OpenCV/MediaPipe hand-gesture tracking for an adaptive learning game. Designed with clinicians in a Mumbai speech clinic.
A diabetes-management app inspired by my twin sister's Type 1 diagnosis. Unifies glucose tracking, carb guidance, emergency alerts, and family monitoring. Pitched to investors at Berkeley's BASICS accelerator.
