Tailin Wu

Tailin Wu is an Assistant Professor at Westlake University in the Department of AI within the School of Engineering. He leads the AI for Scientific Simulation and Discovery Lab, focusing on integrating machine learning with fundamental scientific domains. Previously, he conducted postdoctoral research at Stanford Computer Science (2020-2023) under Jure Leskovec, earned his PhD in Physics from MIT (2019) under advisors Isaac Chuang and Max Tegmark, and completed his BSc in Physics at Peking University (2012).

• Research Interests: Generative AI for scientific simulation (diffusion models, flow matching), AI agents for automated discovery (neural-symbolic integration), representation learning with graph models and information theory

His work spans physics-informed machine learning, with applications in fluid dynamics, energy systems, and life sciences. Key contributions include developing methods for safe PDE control with conformal prediction and creating interpretable theory-learning frameworks through the AI Physicist paradigm.

• Scientific Awards:
  • Best Poster Award, ICML Time Series Workshop (2019)
  • Outstanding Postdoc, Westlake University (2024)
  • Nominated for Outstanding Youth Paper Award, China Embodied AI Conference (2025)

As an educator, he teaches Frontiers in Computer Science and Technology at Westlake University and has guest-lectured at Caltech and MIT. His lab actively recruits postdocs, PhD students, and interns.