DENG Qi

Qi DENG is an Associate Professor in the Department of Data and Business Intelligence at the Antai College of Economics & Management, Shanghai Jiao Tong University. His research focuses on mathematical programming and machine learning, particularly large-scale optimization algorithms and complexity analysis. He has published in leading journals and conferences in operations research and machine learning, including Mathematical Programming, INFORMS Journal on Computing, and ICML. He currently serves as PI on grants from the National Natural Science Foundation of China and the Shanghai Municipal Natural Science Foundation. His research interests include algorithm design, complexity analysis for stochastic optimization, and AI-optimization integration.

Qi Deng is a researcher specializing in optimization and machine learning, with a focus on developing advanced algorithms for convex and nonconvex optimization problems. Their work includes stochastic first-order methods, interior point methods, and trust region methods for various optimization tasks. Deng has contributed to areas such as function-constrained optimization, inventory management, and vortex detection using physics-based techniques. Their research also explores decentralized and gradient-free methods for non-smooth non-convex optimization, as well as low-rank factorization approaches for semidefinite programming. Deng's publications highlight the application of boosting techniques and adaptive algorithms in machine learning and optimization.