陈程

2013.9.-2021.3.    上海交通大学    博士

2016.9.-2017.10.    加州大学伯克利分校    公派联培博士

2009.9.-2013.6.    上海交通大学    本科

2023.4.-至今    华东师范大学    副教授

2021.9.-2023.1.    南洋理工大学    博士后

陈程，华东师范大学软件工程学院副教授。于上海交通大学计算机科学与技术专业本科直博，博士期间在美国加州大学伯克利分校数学系公派联培一年，之后前往新加坡南洋理工大学数学系从事博士后研究工作。主要研究方向包括在线机器学习、最优化、强化学习以及矩阵近似，已在机器学习领域的顶级会议和期刊上发表学术论文十余篇，并获得国家自然科学基金青年科学基金项目资助。目前担任机器学习旗舰期刊JMLR的编委会审稿人 (Editorial board reviewer)，并多次担任NeurIPS、ICML、ICLR等机器学习顶级会议的审稿人。

个人主页：https://chengchen8.github.io/

国际会议审稿人：

• ICML 2021-2023

• NeurIPS 2021-2023

• ICLR 2022-2023

• IJCAI 2023

期刊审稿人：

• Journal of Machine Learning Research

• Transactions on Machine Learning Research

• Frontiers of Computer Science

我的研究目标是设计高效、鲁棒且有理论保证的机器学习算法，主要研究方向包括：

• 在线学习（Online Learning）

• 强化学习（Reinforcement Learning）
  

• 联邦学习（Federated Learning）

• 最优化方法（Optimization Methods）

• 矩阵近似（Matrix Approximation）

欢迎对相关方向感兴趣、自我驱动力强、有较好的数学基础和编程能力的同学加入我的团队。请将你的简历和成绩单发送到我的邮箱chchen@sei.ecnu.edu.cn。

本科生课程：

软件工程数学，2024春

研究生课程：

论文写作，2023秋

机器学习中的优化方法，2023秋

国家自然科学基金青年项目，2024年1月--2026年12月，主持

Conference Publications

• Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales.(Accepted)
  Dapeng Zhi, Peixin Wang, Cheng Chen, Min Zhang.
  The 38th AAAI Conference on Artificial Intelligence (AAAI), 2024. CCF A

• Block Broyden's Methods for Solving Nonlinear Equations.
  Chengchang Liu, Cheng Chen*, Luo Luo, John C.S. Lui.
  The 37th Conference on Neural Information Processing Systems (NeurIPS), 2023. CCF A

• Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks.
  Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang, Cheng Chen, Min Zhang.
  The 37th Conference on Neural Information Processing Systems (NeurIPS), 2023. CCF A

• Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization.
  Luo Luo, Yujun Li, Cheng Chen*.
  The 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. CCF A
  

• Online Active Regression
  Cheng Chen, Yi Li, Yiming Sun.
  The 39th International Conference on Machine Learning (ICML), 2022. CCF A, Long Talk
  

• Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model.
  Cheng Chen, Canzhe Zhao, Shuai Li.
  The 36th AAAI Conference on Artificial Intelligence (AAAI), 2022. CCF A

• Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse. Matrices
  Luo Luo, Cheng Chen*, Guangzeng Xie, Haishan Ye.
  The 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. CCF A

• Efficient Projection-Free Algorithms for Saddle Point Problems.
  Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu.
  The 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. CCF A

• Efficient and Robust High-Dimensional Linear Contextual Bandits.
  Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian.
  The 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020. CCF A

• Efficient Spectrum-Revealing CUR Matrix Decomposition.
  Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu.
  The 23th International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. CCF C

Journal Publications

• Efficient Policy Evaluation by Matrix Sketching.
  Cheng Chen, Weinan Zhang, Yong Yu.
  Frontiers of Computer Science. 16.5 (2022): 1-9. CCF B

• Robust Frequent Directions with Application in Online Learning.
  Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang.
  Journal of Machine Learning Research. 20: 45:1-45:41 (2019). CCF A

• Fast Fisher discriminant analysis with randomized algorithms.
  Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang.
  Pattern Recognition. 72: 82-92 (2017). CCF B

• Multicategory large margin classification methods: Hinge losses vs. coherence functions.
  Zhihua Zhang, Cheng Chen, Guang Dai, Wu-Jun Li, Dit-Yan Yeung.
  Artificial Intelligence. 215: 55-78 (2014). CCF A