Li Wang

Li Wang is an active Associate Professor at the School of Mathematics, University of Minnesota, with a distinguished research career in applied mathematics. His work bridges theoretical analysis and computational methods across multiple scientific domains.

Dr. Wang's research focuses on numerical analysis, scientific computing, applied analysis, kinetic theory, optimal transport, inverse problems, and scientific machine learning. His work demonstrates a strong emphasis on developing numerical methods for partial differential equations with applications in physics, engineering, and data science. He has made significant contributions to asymptotic preserving schemes, gradient flows, and computational methods for kinetic equations.

His publication record shows consistent output in top-tier journals including Journal of Computational Physics, SIAM Journal on Applied Mathematics, and SIAM Journal on Numerical Analysis. The research trends indicate growing integration of machine learning techniques with traditional numerical methods, particularly evident in recent work on DeepONet applications and scientific machine learning approaches to inverse problems and evolution equations.

Dr. Wang actively mentors students and researchers, inviting highly motivated individuals to join his research group. He is organizing significant academic events including a micro-workshop on Data Driven methods in May 2025 and previously organized a Workshop on Kinetic and Optimal Transport in October 2023.

His research group maintains an active presence in the applied mathematics community, with particular strengths in computational methods for kinetic theory and optimal transport problems. The group appears to collaborate extensively with researchers at other institutions, as evidenced by the diverse author lists on recent publications.