research interests

Current Research Interests

• Generative Models: Flow matching and diffusion models, with emphasis on probability-path design, transport dynamics, and scalable sampling for high-dimensional and non-Euclidean data.
• Reinforcement Learning: RL post-training, reasoning, and generative-model fine-tuning for controllable and reliable generation.
• Geometric / Mathematical Machine Learning: Geometry-aware learning and non-Euclidean generative models, including optimal transport and structure-aware modeling.