# Xiangfeng WANG

### Personal Information

- Associate Professor
- School of Computer Science and Technology, East China Normal University
- Email: xfwang at cs.ecnu.edu.cn

### Education

- Ph.D., Computational Mathematics/Optimization, Nanjing Univerisity, 2014 (Supervisor: Professor
**Bingsheng He**) - Visiting Ph.D. student, Optimization, University of Minnesota, 2012-2013 (Supervisor: Professor
**Zhi-Quan Luo**) - B.S., Mathematics, Nanjing University, 2009

### Academic Employment

- 2018-now, Associate Professor, School of Computer Science and Technology, East China Normal University
- 2014-2018, Assistant Professor, School of Computer Science and Technology, East China Normal University

### Research Area

- Distributed Optimization and Applications
- Multi-agent Learning
- Trustworthy Machine Learning: Fairness, Privacy, etc.

### Awards

- 2021 IEEE Signal Processing Society Best Paper Award (for the paper
*Multi-Agent Distributed Large-Scale Optimization by Inexact Consensus ADMM*with Tsung-Hui Chang and Mingyi Hong on*IEEE Transactions on Signal Processing*) - 2022 Shanghai QiMingXing

### Externally Funded Projects

- Model-driven Real-time Control Platform based on ML-driven ODE Solver, QiMingXing Project of Shanghai, STCSM, 2022-2025.
- Research on Advanced Machine Learning Method based on Structured Self-Adaptive and Self-Evolution, National Key Research and Development Program of China, MOST, 2021-2023.
- Research on Machine Learning Algorithms for Distributed Optimization Problems, National Natural Science Foundations of China, NSFC, 2021-2024.
- Research on Trustworthy Machine Learning, Artificial Intelligence Project of Shanghai, STCSM, 2021-2022.
- Virtual Scheduling Algorithm based on Reinforcement Learning, Huawei Project, 2020-2021.
- Distributed Optimization-driven Multi-agent Collaborative Computing Theory and Method, ZhiJiang Project, 2020-2022.
- Research on Structured Algorithm for Large-scale Optimization Problem in Machine Learning, Natural Science Foundation of Shanghai, STCSM, 2019-2022.
- Research on Structured First-order Algorithm for Large-scale Distributed Consensus Optimization Problem, National Natural Science Foundations of China, NSFC, 2016-2018.
- Research on Large-scale Distributed Stochastic Optimization Algorithm, YangFan Project of Shanghai, STCSM, 2015-2017.
- Remark:
**MOST**(Ministry of Science and Technology of the People’s Republic of China);**NSFC**(Natural Science Foundations of China);**STCSM**(Science and Technology Commission of Shanghai Municipality).

### Publications

#### Book and Book Chapter

**王祥丰**/金博, 群体智能/Swarm Intelligence, 人工智能与智能教育丛书, 教育科学出版社.- 吴信东/
**王祥丰**/金博/于政/吴明辉，人机协同/Human-Machine Synergy，科学出版社. - 王晓玲/
**王祥丰**/金博，人工智能伦理与安全(第4章-安全可信人工智能)，清华大学出版社. - X. Fu, B. He,
**Xiangfeng Wang**, and X. Yuan, Block-wise Alternating Direction Method of Multipliers with Gaussian Back Substitution for Multiple-block Convex Programming. Splitting Algorithms, Modern Operator Theory, and Applications, 2019.

#### Journal

- W. Li,
**Xiangfeng Wang**, B. Jin, D. Luo, and H. Zha, Structured Cooperative Reinforcement Learning with Time-varying Composite Action Space. IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted, 2021. **Xiangfeng Wang**, J. Ye, X. Yuan, S. Zeng, and J. Zhang, Perturbation Techniques for Convergence Analysis of Proximal Gradient Method and Other First-order Algorithms via Variational Analysis. Set-Valued and Variational Analysis, 30, 2022, pp.39-79.- J. Sheng, Y. Hu, W. Zhou, L. Zhu, B. Jin, J. Wang, and
**Xiangfeng Wang**, Learning to Schedule Multi-NUMA Virtual Machines via Reinforcement Learning. Pattern Recognition, 121, 2022, pp.108254. - X. Cai,
**Xiangfeng Wang**, and W. Zhang, The O(1/n) Worst-case Convergence Rate of ADMM with Variable Penalty Parameters. Numerical Mathematics A Journal of Chinese Universities, 43(04), 2021, pp.317-334. (Commemorate the 100th anniversary of the birth of Professor Xuchu He) - C. Ma, Q. Xu,
**Xiangfeng Wang**, Bo Jin, X. Zhang, Y. Wang, and Y. Zhang, Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning. IEEE Transactions on Medical Imaging, 40(10), 2021, pp.2563-2574. **Xiangfeng Wang**, J. Yan, B. Jin, and W. Li, Distributed and Parallel ADMM for Structured Nonconvex Optimization Problem. IEEE Transactions on Cybernetics, 51(9), 2021, pp.4540-4552.- Y. Song, T. Liu, T. Wei,
**Xiangfeng Wang**, Z. Tao, and M. Chen, FDA3: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. IEEE Transactions on Industrial Informatics, 17(11), 2021, pp.7830-7838. - W. Liu, C. Shen,
**Xiangfeng Wang**, B. Jin, X. Lu, X. Wang, H. Zha, and J. He, Fairness in Trustworthy Machine Learning: A Survey. Journal of Software, 32(5), 2021, pp.1404-1426. (Chinese version) **Xiangfeng Wang**, J. Zhang, and W. Zhang, The Distance Between Convex Sets with Minkowski Sum Structure: Application to Collision Detection. Computational Optimization and Applications, 77, 2020, pp.465–490.- M. Hong, T.-H. Chang,
**Xiangfeng Wang**, M. Razaviyayn, S. Ma, and Z.-Q. Luo, A Block Successive Upper Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization. Mathematics of Operations Research, 45(3), 2020, pp.833-861. - C. Li,
**Xiangfeng Wang**, W. Dong, J. Yan, Q. Liu, and H. Zha, Active Sample Learning and Feature Selection: A Unified Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6), 2019, pp.1382-1396. - C. Li, F. Wei, W. Dong, Q. Liu,
**Xiangfeng Wang**, and X. Zhang, Dynamic Structure Embedded Online Multiple-output Regression for Streaming Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 2019, pp.323-336. - H. Yue, Q. Yang,
**Xiangfeng Wang**, and X. Yuan, Implementing the ADMM to Big Datasets: A Case Study of LASSO. SIAM Journal on Scientific Computing, 40(5), 2018, pp.A3121-A3156. **Xiangfeng Wang**, W. Zhang, J. Yan, X. Yuan, and H. Zha, On the Flexibility of Block Coordinate Descent for Large-Scale Optimization. Neurocomputing, 272, 2018, pp.471-480.- M. Hong,
**Xiangfeng Wang**, M. Razaviyayn and Z.-Q. Luo, Iteration Complexity Analysis of Block Coordinate Descent Methods. Mathematical Programming Series A, 163, 2017, pp.85-114. - T.-H. Chang, M. Hong, W.-C. Liao, and
**Xiangfeng Wang**, Asynchronous Distributed ADMM for Large-Scale Optimization-Part I: Algorithm and Convergence Analysis. IEEE Transactions on Signal Processing, 64(12), 2016, pp.3118-3130. - T.-H. Chang, W.-C. Liao, M. Hong, and
**Xiangfeng Wang**, Asynchronous Distributed ADMM for Large-Scale Optimization-Part II: Linear Convergence Analysis and Numerical Performances. IEEE Transactions on Signal Processing, 64(12), 2016, pp.3131-3144. **Xiangfeng Wang**, On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities. Journal of the Operations Research Society of China, 3(3), 2015, pp.347-362.**Xiangfeng Wang**, M. Hong, S. Ma, and Z.-Q. Luo, Solving Multiple-Block Separable Convex Minimization Problems using Two-Block ADMM. Pacific Journal of Optimization, 11(4), 2015, pp.645-667.- T.-H. Chang, M. Hong, and
**Xiangfeng Wang**, Multi-Agent Distributed Large-Scale Optimization by Inexact Consensus ADMM. IEEE Transactions on Signal Processing, 63(2), 2015, pp.482-497.**ESI高被引论文****IEEE Signal Processing Society Best Paper Award** - X. Luo,
**Xiangfeng Wang**, Z. Suo, and Z. Li, Efficient InSAR Phase Noise Reduction via Total Variation Regularization. Science China (Information Sciences), 2015, 58(8), 1-13. **Xiangfeng Wang**, and X. Yuan, The Linearized Alternating Direction Method of Multipliers for Dantzig Selector. SIAM Journal of Scientific Computing, 34(5), 2012, pp.A2792-A2811.

#### Conference

- J. Sheng, S. Cai, H. Cui, W. Li, Y. Hua, B. Jin, W. Zhou, Y. Hu, L. Zhu, Q. Peng, H. Zha and
**Xiangfeng Wang**, VMAgent: A Practical Virtual Machine Scheduling Platform. IJCAI-Demos, 2022. (**The VMAgent Simulator**) - W. Li, H. Chen, B. Jin, W. Tan, H. Zha,
**Xiangfeng Wang**, Multi-Agent Path Finding with Prioritized Communication Learning. ICRA, 2022. - T. Wu, W. Li, B. Jin, W. Zhang and
**Xiangfeng Wang**, Weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition. DASFAA, 2022. - W. Li,
**Xiangfeng Wang**, B. Jin, J. Sheng and H. Zha, Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition. ICLR, 2022. - Y. Hua,
**Xiangfeng Wang**, B. Jin, W. Li, J. Yan, X. He, and H. Zha, Hyper-Meta Reinforcement Learning with Sparse Reward. KDD, 2021. - Q. Xu, Q. Wu, Y. Hu, B. Jin, B. Hu, F. Zhu, Y. Li, and
**Xiangfeng Wang**, Semi-supervised Medical Image Segmentation with Confidence Calibration. IJCNN, 2021. - W. Li,
**Xiangfeng Wang**, B. Jin, J. Sheng, Y. Hua, and H. Zha, Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning. AAMAS, 2021. - X. Li,
**Xiangfeng Wang**, B. Jin, W. Zhang, J. Wang, and H. Zha, VSB$^2$-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing. ICPR, 2020. - J. Wang,
**Xiangfeng Wang**, B. Jin, J. Yan, W. Zhang, and H. Zha, Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning. ICPR, 2020. - X. Liao, W. Li, Q. Xu,
**Xiangfeng Wang**, B. Jin, X. Zhang, Y. Zhang, and Y. Wang, Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning. CVPR, 2020. - X. Li, X. Wen, B. Jin,
**Xiangfeng Wang**, J. Wang, and J. Cai, Visual-to-Semantic Hashing for Zero-shot Learning. IJCNN, 2020. - Y. Xie,
**Xiangfeng Wang**, R. Wang, and H. Zha, A Fast Proximal Point Method for Computing Exact Wasserstein Distance. UAI, 2019. - M. Zhang, C. Li, and
**Xiangfeng Wang**, Multi-View Metric Learning for Multi-Label Image Classification. ICIP, 2019. - W. Li, B. Jin, and
**Xiangfeng Wang**, SparseMAAC: Sparse Attention for Multi-agent Reinforcement Learning. DASFAA, 2019. - W. Zhang, J. Yan,
**Xiangfeng Wang**, and H. Zha, Deep eXtreme Multi-label Learning. ICMR, 2018. - X. Liu, J. Yan, S. Xiao,
**Xiangfeng Wang**, H. Zha, and S. Chu, On Predictive Patent Valuation: Forecasting Patent Citations and Their Types. AAAI, 2017. - T.-H. Chang, M. Hong, W.-C. Liao, and
**Xiangfeng Wang**, Asynchronous Distributed Alternating Direction Method of Multipliers: Algorithm and Convergence Analysis. ICASSP, 2016. - D. Hajinezhad, T.-H. Chang,
**Xiangfeng Wang**, Q. Shi, and M. Hong, Nonnegative Matrix Factorization using ADMM: Algorithm and Convergence Analysis. ICASSP, 2016. - S. Xiao, J. Yan, C. Li, B. Jin,
**Xiangfeng Wang**, H. Zha, X. Yang, and S. Chu, On Modelling and Predicting Individual Paper Citation Count Over Time. IJCAI, 2016. - J. Yan, S. Xiao, C. Li, B. Jin,
**Xiangfeng Wang**, H. Zha, and X. Yang, Modelling Contagious M$\&$A via Point Processes with a Profile Regression Prior. IJCAI, 2016. - C. Li, F. Wei, W. Dong,
**Xiangfeng Wang**, J. Yan, X. Zhu, Q. Liu, and X. Zhang, Spatially Regularized Streaming Sensor Selection. AAAI, 2016. **Xiangfeng Wang**, M. Hong, T.-H. Chang, M. Razaviyayn, and Z.-Q. Luo, Joint Day-Ahead Power Procurement and Load Scheduling using Stochastic ADMM. ICASSP, 2014.- H.-W. Tseng, S. Vishnubhotla, M. Hong,
**Xiangfeng Wang**, J. Xiao, Z.-Q. Luo, and T. Zhang, A Single Channel Speech Enhancement Approach by Combining Statistical Criterion and Multi-Frame Sparse Dictionary Learning. INTERSPEECH, 2013.

### Teaching

- Multi-agent System (Multi-agent Reinforcement Learning)
- Optimization for Machine Learning