About
Shih-Hsin Wang 王士欣
Assistant Professor · CSIE & AICoRE, National Taiwan University
- Office: CSIE Building (德田館) R516 · Lab: CSIE Building R303
- shwang@csie.ntu.edu.tw
- Google Scholar
- GitHub
I am an Assistant Professor in the Department of Computer Science and Information Engineering (CSIE) at National Taiwan University, with joint appointments in the Graduate Institute of Networking and Multimedia (INM) and the AI Center of Research Excellence (AICoRE). I am also a Yushan Young Fellow, supported by the Yushan Fellow Program of Taiwan’s Ministry of Education.
I received my B.S. in Mathematics from National Taiwan University (2016–2020) and my Ph.D. in Mathematics from the University of Utah (2021–2026), where I was advised by Bao Wang and Tommaso de Fernex.
My work spans geometric deep learning, generative models (e.g., flow matching & diffusion 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.
I received my B.S. in Mathematics from National Taiwan University (2016–2020) and my Ph.D. in Mathematics from the University of Utah (2021–2026), where I was advised by Bao Wang and Tommaso de Fernex.
My work spans geometric deep learning, generative models (e.g., flow matching & diffusion 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.
Approach
Theory & Knowledge → Foundations
Building foundations through mathematical guarantees and the integration of domain knowledge to ensure reliability and efficiency.
↔
Practical Solution → Application
Translating theoretical foundations into efficient, controllable AI systems that deliver reliable performance in real-world settings.