Prof. Qingna Li | Numerical Optimization | Best Researcher Award

Professor in Numerical Optimization | Beijing Institute of Technology | China

Prof. Qingna Li is a leading scholar in computational mathematics and optimization, widely recognized for her influential contributions to numerical algorithms, matrix optimization, support vector machines, hyperparameter tuning through bilevel optimization, large-scale MIMO detection, and correlation matrix modeling. Her research bridges theoretical rigor with practical impact, with applications spanning signal processing, machine learning, classification systems, hypergraph matching, molecular conformation, and radar surveillance analysis. She has produced a strong body of high-impact work, reflected in a growing citation footprint: Google Scholar reports 489 citations, an h-index of 10, and 11 i10-index publications, while Scopus documents also reflect sustained citation growth and scholarly impact across optimization, numerical analysis, and applied machine learning. Professor Li has developed several widely used MATLAB packages for SVM models, correlation matrix estimation, and MIMO detection, each grounded in her peer-reviewed publications and enabling significant uptake by researchers and practitioners. Her work on derivative-free methods, semismooth Newton frameworks, quadratic programming relaxations, and data-driven wavelet approaches has strengthened modern optimization theory and advanced computational tools for high-dimensional problems. She is an active researcher with a documented record of collaborative publications in top journals such as SIAM Journal on Optimization, IMA Journal of Numerical Analysis, Computational Optimization and Applications, and Applied and Computational Harmonic Analysis. With a demonstrated ability to generate impactful methods, lead research groups, and contribute meaningfully to the global optimization community, Professor Qingna Li is exceptionally well suited for a Best Researcher Award, given her innovation, citation influence, research leadership, and sustained contributions to computational optimization.

Profile

Google Scholar

Featured Publications

Li, Q., & Li, D. H. (2011). A class of derivative-free methods for large-scale nonlinear monotone equations. IMA Journal of Numerical Analysis, 31(4), 1625–1635. Citations: 192.

Li, Q., & Qi, H. (2011). A sequential semismooth Newton method for the nearest low-rank correlation matrix problem. SIAM Journal on Optimization, 21(4), 1641–1666. Citations: 50.

Yin, J., & Li, Q. (2019). A semismooth Newton method for support vector classification and regression. Computational Optimization and Applications, 73(2), 477–508. Citations: 33.

Zhao, P. F., Li, Q., Chen, W. K., & Liu, Y. F. (2021). An efficient quadratic programming relaxation-based algorithm for large-scale MIMO detection. SIAM Journal on Optimization, 31(2), 1519–1545. Citations: 9.

Pang, T., Li, Q., Wen, Z., & Shen, Z. (2020). Phase retrieval: A data-driven wavelet frame-based approach. Applied and Computational Harmonic Analysis, 49(3), 971–1000. Citations: 11.