DeerMe.AI — Camera-Based AI Physical Coaching
Co-founded and led the technical side of a mobile coaching app that reads human keypoints to give real-time form feedback. React Native client, Python vision services, 39 automated tests.
S.M. student in Data Science at Harvard. I work on multimodal foundation models, video world models, and structured generative modeling.
Open to Summer 2027 research / ML internships
I study how structured representations can make multimodal and generative models more capable, from non-Euclidean geometry to action-conditioned video generation.
Recent work covers Flow Matching, hierarchical tokenization, pretrained VLM adaptation, and world models for robot planning.
Extends multinomial logistic regression to umbilic Riemannian geometries, giving one classifier for non-Euclidean representation learning.
Paper and code coming soon
A full neural-network framework for the unconstrained Proper Velocity model. Best MCC on all five TEB benchmarks, with an 8.3-point gain over HCNN-S on SINEs.
Co-founded and led the technical side of a mobile coaching app that reads human keypoints to give real-time form feedback. React Native client, Python vision services, 39 automated tests.