Portfolio
Selected work from the lab — peer-reviewed research that anchors our applied practice.
Research
From Partially to Fully Unmatched Modalities as Negative Samples in Contrastive Learning
We introduce a curriculum for multimodal contrastive learning that treats partially-to-fully unmatched modality pairs as progressively harder negative samples, improving cross-modal representation quality.
Toward an Open Ecosystem for Federated Learning Mobile Apps
FLSys is a mobile-cloud federated learning system that supports different FL models and aggregation methods, enabling an open ecosystem of privacy-preserving FL apps on smartphones.
Low-Overhead Model Pruning for Federated Learning
Complement Sparsification prunes models through complementary, collaborative sparsification at the server and the clients, cutting communication and computation overhead in federated learning.
Engineering
Applied AI delivery runs through Meterra, our engineering arm. Case studies are available on request.