Qi Deng is currently an Associate Professor at the Antai College of Economics and Management in Shanghai Jiao Tong University. Previously, he worked at the School of Information Management and Engineering in Shanghai University of Finance and Economics, where he served as an Assistant Professor and later as an Associate Professor. He holds a Ph.D. in Computer Science from the University of Florida and a Bachelor’s degree in Computer Science from Shanghai Jiao Tong University. His primary research interests include mathematical programming and machine learning. In recent years, his research has been published in journals and conferences in the fields of management, optimization, and machine learning, including Mathematical Programming, POMS, IJOC, ICML and NeurIPS.

Research interests:  Algorithm design and complexity analysis for stochastic optimization, nonsmooth optimization, and constrained optimization; integrated learning and optimization, energy and power system

Recent related work

Lin, Z. & Deng, Q. (2024) Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints. NeurIPS 2024

Deng, Q., Feng, Q., Gao, W., Ge, D., Jiang, B., Jiang, Y., Liu, J., Liu, T., Xue, C., & Ye, Y. (2024). An Enhanced ADMM-based Interior Point Method for Linear and Conic Optimization. Informs Journal on Computing.

Lin, Z., Xue, C., Deng, Q., & Ye, Y. (2024). A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes. ICML 2024. 

Processes

Gao, W., & Deng, Q. (2024). Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity. ICML 2024.

Tan, J., Xue, C., Zhang, C., Deng, Q., Ge, D., & Ye, Y. (2024). A Homogenization Approach for Gradient-Dominated Stochastic Optimization.UAI 2024. 

Xie, C., Li, C., Zhang, C., Deng, Q., Ge, D., & Ye, Y. (2024). Trust region methods for nonconvex stochastic optimization beyond Lipschitz smoothness. Proceedings of the AAAI Conference on Artificial Intelligence, 

Liu, J., Xie, C., Deng, Q., Ge, D., & Ye, Y. (2024). Sketched Newton Value Iteration for Large-Scale Markov Decision Processes. Proceedings of the AAAI Conference on Artificial Intelligence, 

Lin, Z., Xia, J., Deng, Q., & Luo, L. (2024). Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 

Han, Q., Li, C., Lin, Z., Chen, C., Deng, Q., Ge, D., Liu, H., & Ye, Y. (2024). A Low-Rank ADMM Splitting Approach for Semidefinite Programming. arXiv preprint arXiv:2403.09133. 

Gao, W., & Deng, Q. (2024). Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization. Advances in Neural Information Processing Systems, 36. 

Boob, D., Deng, Q., & Lan, G. (2024). Level constrained first order methods for function constrained optimization. Mathematical programming, 1-61. 

Shi, Z., Wang, X., Yan, D., Chen, S., Lin, Z., Xia, J., & Deng, Q. (2023). An accelerated primal‐dual method for semi‐definite programming relaxation of optimal power flow. IET Energy Systems Integration, 5(4), 477-490. 

Liu, J., Xie, C., Deng, Q., Ge, D., & Ye, Y. (2023). Stochastic Dimension-reduced Second-order Methods for Policy Optimization. arXiv preprint arXiv:2301.12174. 

Lin, Z., & Deng, Q. (2023). Gbm-based bregman proximal algorithms for constrained learning. arXiv preprint arXiv:2308.10767. 

Boob, D., Deng, Q., & Lan, G. (2023). Stochastic first-order methods for convex and nonconvex functional constrained optimization. Mathematical programming, 197(1), 215-279. 

Boob, D., & Deng, Q. (2023). First-order methods for Stochastic Variational Inequality problems with Function Constraints. arXiv preprint arXiv:2304.04778. 

Deng, Q., & Gao, W. (2021). Minibatch and momentum model-based methods for stochastic weakly convex optimization. Advances in Neural Information Processing Systems, 34, 23115-23127. 

邓琪, 高建军, 葛冬冬, 何斯迈, 江波, 李晓澄, 王子卓, 杨超林, & 叶荫宇. (2020). 现代优化理论与应用. 中国科学: 数学, 50(7), 899-968. 

Deng, Q., & Lan, C. (2020). Efficiency of coordinate descent methods for structured nonconvex optimization. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 

Boob, D., Deng, Q., Lan, G., & Wang, Y. (2020). A feasible level proximal point method for nonconvex sparse constrained optimization. Advances in Neural Information Processing Systems, 33, 16773-16784. 

Tan, Y., Paul, A. A., Deng, Q., & Wei, L. (2017). Mitigating inventory overstocking: Optimal order‐up‐to level to achieve a target fill rate over a finite horizon. Production and Operations Management, 26(11), 1971-1988. 

Data structures (Planning)