Ren-Cang Li

Ren-Cang Li is a Professor of Mathematics at The University of Texas at Arlington, with affiliations including past roles as Chair Professor at Hong Kong Baptist University and positions at University of Kentucky and Oak Ridge National Laboratory. He earned his BS from Xiamen University (1985), MS from Chinese Academy of Sciences (1988), and PhD in Applied Mathematics from UC Berkeley (1995). His research focuses on numerical linear algebra, eigenvalue problems, high-performance computing, and machine learning, with impactful contributions like secular equation solvers in LAPACK and work on matrix equations. Awards include the NSF CAREER Award (1999) and multiple distinguished paper awards.

Education:

• PhD in Applied Mathematics, UC Berkeley, 1995
• MS in Computational Mathematics, Chinese Academy of Sciences, 1988
• BS in Computational Mathematics, Xiamen University, 1985

Research interests span numerical algorithms, eigenvalue computations, optimization, and applications in machine learning. Notable contributions include methods for large-scale eigenvalue problems, matrix equations, and compressed sensing. His work on the secular equation solver is foundational in scientific computing libraries.

Key Awards:

• Friedman Memorial Prize in Applied Mathematics (1996)
• NSF CAREER Award (1999)
• Three Distinguished Paper Awards from ICCM (2018-2020)
• INFORMS Data Mining Best General Paper (2019)

Advising and Grants: Supervised numerous PhD students and led federally funded projects totaling over $5M, including work on probabilistic graph learning and nonlinear matrix equations. Active in editorial roles for journals like SIAM Journal on Matrix Analysis and Mathematical Communications.