World Model Team Lead @ Function Health · CS PhD Adv @ Berkeley · ex-Tera AI · ex-Google · Advisor @ Visia AI
I lead multi-modal, self-supervised AI research for real-world positive impact 🌱
MICCAI 2026
Combines healthy-skin inpainting, rare-lesion mapping, and disease-conditioned diffusion to create 266k+ synthetic dermatology images across skin tones and improve malignancy classification.
90.9% DDI malignancy accuracy, with equality of opportunity improving from 69.6% to 86.6% versus PatchAlign after real-data fine-tuning.
MICCAI 2026
Adapts a monocular 3D model with synthetic D-Synth data and small clinical datasets to recover metric lesion geometry and detailed surface normals from one image.
Synthetic training reduces SKINL2 depth-scale overestimation from 16.1× to 1.11× while fine-tuning only 0.6% of model parameters.
arXiv · 2025Preprint
Trains a billion-parameter world model on street-view images linked by location, time, and movement commands, unifying controlled image generation, geolocation, and action prediction.
60% of geolocation predictions fall within 10 meters on STRIDE’s San Mateo test data.
