Prof. Qingna Li | Mathematics | Research Excellence Award
Professor | Beijing Institute of Technology | China
Prof. Qingna Li is widely recognized for her influential contributions to Mathematics, with her research consistently advancing the global understanding of optimization theory and computational methods within Mathematics. Her work integrates rigorous analytical frameworks in Mathematics with practical algorithmic design, resulting in high impact publications that strengthen interdisciplinary applications of Mathematics. She has produced an extensive body of research across leading international platforms, demonstrating strong visibility in Mathematics and sustained engagement with collaborative projects that rely heavily on mathematical modeling. Her expertise in Mathematics has supported innovative developments in optimization algorithms, numerical strategies, and data driven analytical tools that continue to influence scholars working across diverse areas connected to Mathematics. She has collaborated with multiple research groups and professional networks, further extending the societal relevance of Mathematics through contributions that support technology, engineering, and computational research communities. Her commitment to Mathematics is also reflected in her leadership within research teams and in mentoring emerging scholars who pursue advanced studies grounded in Mathematics. Her academic record highlights a strong publication profile and measurable research influence that underscores the growing global relevance of Mathematics in contemporary scientific inquiry. Google Scholar Profile Of Citations 492, h index 10, i10 index 11.
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Featured Publications
A class of derivative-free methods for large-scale nonlinear monotone equations
IMA Journal of Numerical Analysis, 2011 •
Cited by 193
A sequential semismooth Newton method for the nearest low-rank correlation matrix problem
SIAM Journal on Optimization, 2011 •
Cited by 50
A semismooth Newton method for support vector classification and regression
Computational Optimization and Applications, 2019 •
Cited by 33
An efficient augmented Lagrangian method for support vector machine
Optimization Methods and Software, 2020 •
Cited by 22
Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method
Mathematical Methods of Operations Research, 2022 •
Cited by 18