San Francisco Bay Area
I study CS (BS/MS) at Stanford with a sprinkle of math and English. My technical interests lie in deep learning and distributed computing, and I’ve done a lot of related research engineering work. At NVIDIA, I developed generative world models with multi-camera fusion to simulate tail risk, advancing the robustness of our AV fleet. At Jane Street, I used deep learning to find interpretable signals in new forms of text and options data. My research with Stanford’s Integrated Mental Health Lab was recently accepted to Nature Neuroscience, and my software engineering at various Series A startups (Beacons, TeachFX) has launched new features 0-1 to millions of users. I have a broad passion for socially impactful tech ranging from education (TeachFX) to mental health (Aprendi, IMHL) to housing justice (AEMP) and civic work (Interpreta, STV). Looking for ways to nurture human solutions to social problems with technology. Outside of work, I find inspiration in solarpunk, creative nonfiction, Mandopop, industrial sociology, topology (kinda), and Spider-Man!
Multi-Modal + Search Stuff
Building AI agents for code!
Social and Language Technologies (SALT) Lab: Language Agents for Social Training Stanford Trustworthy AI Research (STAIR) Lab: LLMs for Low-Resource Languages
Autonomous driving, World-Model Team: Building multi-camera sensor fusion for generative world model
Trained LLM to extract relevant stocks from news. Built interpretable deep autoencoder for options trading.