Jiawang Nie

Jiawang Nie is a Professor of Mathematics at the University of California, San Diego, where he conducts research at the intersection of optimization, algebraic geometry, and computational mathematics. His work focuses on developing theoretical foundations and practical algorithms for solving complex polynomial optimization problems with applications across various scientific domains.

His primary research interests include Polynomial and Semidefinite Optimization, Data Science Optimization, and Matrix and Tensor Computation. These areas form the backbone of his theoretical contributions to optimization theory, with particular emphasis on moment problems, sum of squares methods, and applications to tensor computations and machine learning.

Nie's recent publications demonstrate a consistent focus on advancing the theory and applications of polynomial optimization. His work spans from fundamental theoretical developments in moment problems and semidefinite programming to practical applications in multi-view learning, tensor decomposition, and game theory. A notable trend is his increasing focus on connecting optimization theory with machine learning applications, particularly in handling high-dimensional data structures through tensor methods.

Professor Nie is actively involved in the academic community through teaching and seminar organization. He teaches graduate courses including Math 271C (Nonlinear Nonconvex Optimization) and runs the UCSD Optimization and Data Science Seminar (Math 278C), which has been ongoing since at least Winter 2017 and continues into Spring 2025. He authored the book Moment and Polynomial Optimization (SIAM, 2023), which serves as a comprehensive resource in the field.

His academic contributions extend beyond publications to educational resources, with numerous graduate-level courses taught at UCSD covering optimization theory and applications. His research demonstrates a sustained commitment to advancing both the theoretical foundations and practical applications of optimization methods in mathematics and computational science.