Projectsedit

Research project clustersedit

AI and networksedit

AI and Networks is the current primary project cluster. It includes data pruning for decentralized learning, communication-aware evaluation, cross-silo reliability, and distributed computation for Wasserstein-style distributional references.

Distributed Wasserstein barycentersedit

Distributed Wasserstein barycenter is the current doctoral focus within the AI-and-networks cluster. It asks how multiple parties can compute or approximate a shared distributional reference from local empirical measures, with applications to collaborative evaluation, sample scoring, and synthetic-data verification.

Machine unlearningedit

Machine Unlearning includes both approximate certified unlearning for differentiable models and exact or efficient unlearning for tree ensembles. Project pages include Hessian-Free Online Certified Unlearning, Beyond Binary Erasure: Soft-Weighted Unlearning for Fairness and Robustness, and DynFrs: An Efficient Framework for Machine Unlearning in Random Forest.

Collaborative evaluationedit

Collaborative Evaluation studies verification without raw-data exchange. It is used in the ICML 2026 model-collapse work to replace a single biased verifier with multi-party Wasserstein-geometry proxies.

Synthetic dataedit

Synthetic Data asks when generated data can safely replace or augment real data, and when recursive training amplifies bias or erodes diversity. The main paper page is When Sample Selection Bias Precipitates Model Collapse.