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Mingda WAN Short BioI am very fortunate to be advised by Prof. Tao LIN at LINs Lab. Previously, I worked with Prof. Yingyu Liang and Dr. Zhenmei Shi at UW–Madison. My research interests mainly focus on intersection of generative modeling and theoretical machine learning.
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Selected Publications* denote alphabetical order. |
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Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song*, Mingda Wan*, and Yufa Zhou* ICCV 2025 paper |
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NRFlow: Towards Noise-Robust Generative Modeling via High-Order Flow Matching
Bo Chen*, Chengyue Gong*, Xiaoyu Li*, Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song*, Mingda Wan*, and Xugang Ye* UAI 2025 paper |
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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling
Yang Cao*, Bo Chen*, Xiaoyu Li*, Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song* and Mingda Wan* CIKM 2025 paper |
Academic Service |
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External Reviewer
Research Grants Council (Hong Kong) |
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Reviewer
ICCV 2025 KDD 2024 |
HobbiesDuring my spare time, you can always find me jogging, swimming, and fitness. I am also obsessed with classical novels, cinema, and progressive rock. |
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Design and source code from Jon Barron. |