Full Professor, School of Engineering and Applied Sciences, Harvard University
1 paper at NeurIPS 2025
Energy Matching unifies flow matching and energy-based models in a single time-independent scalar field, enabling efficient transport from noise to data while retaining explicit likelihood information for flexible, high-quality generation.
