Assistant Professor · CSIE, National Taiwan University
Shih-Hsin Wang 王士欣
I develop machine learning methods that are both theoretically grounded and practical, spanning geometric deep learning, generative models, and AI for Science. I care about turning intuition into principled, reliable, and efficient tools — and applying them to problems in molecules, biology, and other sciences.
Recent
Experience →Aug 2026
Undergraduate project places are full for now; MS/PhD depends on capacity. See Join the Lab for where things stand and how to apply.
Jul 2026
I was named a Yushan Young Fellow by Taiwan's Ministry of Education (Yushan Fellow Program).
Jul 2026
I am organizing two minisymposia on generative models, transport, and inverse problems at SIAM IS26 & MDS26 (Salt Lake City, Nov 2026). [Details]
Jul 2026
I will join the Department of Computer Science and Information Engineering at National Taiwan University as an Assistant Professor in August 2026.
Notes
All notes → Last read Continue →
Courses
Diffusion Models and Their Applications Starting from diffusion models and flow matching, extending to discrete generation, one-step generative models, and applications across science and engineering. Mathematical Foundations A plain-language introduction to the mathematical tools that recur in machine learning, and to the mathematics the other courses on this site rely on. Machine Learning Foundations The core concepts, methods and practice of machine learning — the basis for understanding, analyzing and applying different models.
Research Areas
Academic Skills
Research
- Geometric Deep Learning Equivariant graph neural networks and expressive geometric graph representations.
- Generative Models Diffusion models, flow matching, single-step models, and test-time guidance.
- AI for Science Modeling complex structures like molecules and proteins.
Selected publications
All publications →ICML 2026 GenBio [Spotlight]
ICML 2026