赵友，西南大学电子信息工程学院讲师。2023 年 6 月于重庆大学计算机科学与技术专业获博士学位，随后加入西南大学，主要从事分布式加速神经动力优化与智能电网调度领域的理论及应用研究。近年来，主持/参与国家自然科学基金项目 5 项、横向项目 2 项；在 JMLR、IEEE TNNLS、IEEE TSMCS、IEEE TNSE、IEEE TCNS、IEEE TCE、IEEE TETCI、IEEE/CAA J. Autom. Sin.、Neural Netw.、Neural Comput.、Sci China Tech Sci. 等国际著名 SCI 期刊发表论文30 余篇；荣获重庆市优秀博士学位论文奖、2024 年ACM 重庆分会优秀博士论文奖、2024 3rd International Conference on Neuromorphic Computing (ICNC 2024)和The 7th International Conference on New Trends in Computational Intelligence最佳论文奖等荣誉；现任 Journal of Artificial Intelligence & Control Systems、Applied Mathematics and Statistics 及 AI and Autonomous Systems 期刊青年编委、重庆大学校友创新创业创投联合会校友博士团副秘书长；同时担任 IEEE TNNLS、IEEE TAC、IEEE TCYB、IEEE TII、IEEE TSMCS、IEEE TNSE、Mathematics of Operations Research等多个国际期刊的审稿人。

主要科研项目：

1. 国家自然科学基金青年基金：稀疏信号重构的分布式加速神经动力学优化方法研究，2025.01-2027.12，主持

2. 横向项目：神经动力学优化算法程序开发，2024.12-2026. 12，主持

3. 中央高校基本科研业务费一般项目学生项目，基于ADMM算法的智能电网动态经济分配问题研究，2017.01-2017.12，主持

4. 国家自然科学基金联合基金重点项目， 未知地下空间复杂环境无人系统集群协同勘测方法研究，2026.01-2029.12，主研

5. 国家自然科学基金面上项目，基于神经动力学算法的多微网互联模式下需求响应策略，2018.01-2021.12，已结题，主研

6. 国家自然科学基金面上项目，基于视觉感知的超清视频编码失真评估与优化研究， 2022.01-2024.12，主研

7.横向项目：重庆蚂蚁消费金融有限公司校企合作项目，金融反欺诈中的人证识别研究，2022.04-2023.04，主研

代表性学术论文：

1.Y. Zhao, X. Liao, X. He, M. Zhou, and C. Li “Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems,” Journal of Machine Learning Research, vol. 24. no. 343, pp. 1-59, 2023.

2.Y. Zhao, X. Liao, and X. He, “Fixed-time Stable Neurodynamic flow to Sparse Signal Recovery via Nonconvex L1-β2-norm,” Neural Computation, vol. 34, pp. 1–29, 2022.

3.Y. Zhao, X. Liao, X. He, and R. Tang, “Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L1-Minimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7488-7501, Dec. 2022.

4.Y. Zhao, X. He, M. Zhou, J. Yu, T. Huang. “Distributed Projection Neurodynamic Approaches in Continuous and Discrete Time for Sparse Recovery with Block Decomposition of Observation Matrix,” IEEE Transactions on Neural Networks and Learning Systems.

5.Y. Zhao, X. Liao, and X. He, “Distributed Smoothing Projection Neurodynamic Approaches for Constrained Nonsmooth Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 675-688, Feb. 2023.

6.Y. Zhao, X. Liao, and X. He, “Distributed Continuous and Discrete Time Projection Neurodynamic Approaches for Sparse Recovery,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 6, pp. 1411-1426, Dec. 2022.

7.Y. Zhao, X. Liao, M. Zhou, and X. He, “Distributed Computation for Sparse Recovery via Continuous-Time Neurodynamic Approach,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 3372-3383, Feb. 2024.

8.Y. Zhao, X. Liao, and X. He, “Accelerated Projection Algorithm Based on Smoothing Approximation for Distributed Non-smooth Optimization,” IEEE Transactions on Control of Network Systems, vol. 10, no. 4, pp. 1682-1694, Dec. 2023.

9.Y. Zhao, X. He, M. Zhou, and T. Huang, “Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems,” IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 6, pp. 1485-1498, June 2024.

10.Y. Zhao, X. He, M. Zhou, J. Yu, and T. Huang, “Distributed Inertial Proximal Neurodynamic Approach for Sparse Recovery on Directed Networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 10, pp. 6180–6193. 2024.

11.Y. Zhao, X. Liao, and X. He, “Distributed Inertial Continuous and Discrete time Algorithms for Solving Resource Allocation Problem,” IEEE Transactions on Network Science and Engineering, vol. 10, no. 6, pp. 3131-3143, Nov-Dec. 2023.

12.Y. Zhao, Z. Allen-Zhao, L. Wang, X. He, and Q. Mao, “Inertial Primal-Dual Projection Neurodynamic Approaches for Constrained Convex Optimization Problems and Application to Sparse Recovery. Neural Networks. 186: 107274. 2025.

13.Y. Zhao, X. Liao, and X. He, “Novel Projection Neurodynamic Approaches for Constrained Convex Optimization,” Neural Networks, vol.150, pp. 336–349, 2022.

14.Y. Zhao, X. Liao, X. He, R. Tang, and W. Deng, “Smoothing Inertial Neurodynamic Approach for Sparse Signal Reconstruction via Lp-norm Minimization,” Neural Networks, vol. 140, pp. 100–112, 2021.

15.Y. Zhao, X. He, T. Huang, and J. Huang, “Smoothing Inertial Projection Neural Network for Minimization Lp-q in Sparse Signal Reconstruction,” Neural Networks, vol. 99, pp. 31–41, 2018.

16.Y. Zhao, X. He, T. Huang, J. Huang, and P. Li, “A Smoothing Neural Network for Minimization L1-Lq in Sparse Signal Reconstruction with Measurement Noises,” Neural Networks, vol. 122, pp. 40–53, 2020.

17.Y. Zhao, X. He, Y. Yao, and J. Huang, “Plug-in Electric Vehicle Charging Management via a Distributed Neurodynamic Algorithm,” Applied Soft Computing, vol. 80, pp. 557-566, 2019.

18.Y. Zhao, X. He, J. Yu, and T. Huang, “Distributed Accelerated Primal-Dual Neurodynamic Approaches for Resource Allocation Problem,” Science China Technological Sciences, vol. 66, no. 12, pp. 3639–3650, 2023.

19.Y. Zhao, X. He, T. Huang, and Q. Han, “Analog Circuits for Solving a Class of Variational Inequality Problems,” Neurocomputing, vol. 295, pp. 142–152, 2018.

20.Y. Zhao, X. He, H. Che and H. Li, “Accelerated Projection Neurodynamic Approach for Convex Optimization with Affine Equality and Inequality Constraints,” 2024 International Conference on Neuromorphic Computing (ICNC), Chongqing, China, 2024, pp. 1-4.