CSML 2026 will be held from August 7 to August 9, 2026 at Chen Ruiqiu Building, Shanghai Jiao Tong University.
The conference focuses on the following three aspects on machine learning. Mathematic machine learning: study machine learning from mathematic perspective, such as theory and model. Scientific machine learning: the application of ML in scientific problems and design of ML algorithms inspired by scientific computing. Industrial machine learning: the application of ML in industry.
Organization of the
Conference
Conference Chairs
E Weinan
(Academician, Peking University)
Jin Shi
(Professor, Shanghai Jiao Tong University)
Conference Academic Committee (in alphabetical order by surname)
Bao Gang
(Academician, Zhejiang University)
E Weinan
(Academician, Peking University)
Gao Xiaoshan
(Researcher, Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
Gong Xingao
(Academician, Fudan University)
Jiang Song
(Academician, Beijing Institute of Applied Physics and Computational Mathematics)
Jin Shi
(Professor, Shanghai Jiao Tong University)
Liu Weidong
(Professor, Shanghai Jiao Tong University)
Ma Zhiming
(Academician, Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
Fee: No registration fee is required for the conference.
Accommodation: Participants should arrange accommodation by themselves.
Relevant Notifications
1. Conference Schedule
The conference will be held from August 7 to 9, 2026, at Chen Ruiqiu Building, Shanghai Jiao Tong University. August 7 is for registration, with conference sessions on the following two days. Four invited talks by young scholars will be arranged in the two mornings. A viewpoint forum will be arranged on the morning of the first day. Four rounds of parallel sessions will be arranged, with 7–8 sessions in each round. Each talk in the parallel sessions is 30 minutes. Around 30 parallel sessions and about 120 speakers are expected. The expected number of participants is 500–600.
2. Accommodation and Catering Arrangements
There is no registration fee. Accommodation should be arranged by participants themselves.
Contact Information
Luo Tao: luotao41@sjtu.edu.cn
Organizers:
Wang Liwei, School of Intelligence, Peking University
Speakers:
Li Tongyang, School of Computer Science, Peking University
Wu Lei, School of Mathematical Sciences, Peking University
Li Jian, Institute for Interdisciplinary Information Sciences, Tsinghua
University
Wei Zhewei, Gaoling School of Artificial Intelligence, Renmin University of
China
2. Recent Advances in Machine Learning Theory (I, II)
Organizers:
Wu Lei, Peking University
Speakers:
Liu Fanghui, University of Warwick
Liao Zhenyu, Huazhong University of Science and Technology
Long Jihao, Shanghai Algorithmic Innovation Institute
Zhang Jingzhao, Tsinghua University
Fang Cong, Peking University
Yuan Yang, Tsinghua University
Cao Yuan, The University of Hong Kong
Luo Tao, Shanghai Jiao Tong University
3. Recent Advances on Learning to Optimize
Organizers:
Liang Jingwei, Shanghai Jiao Tong University
Speakers:
Yin Wotao, Alibaba DAMO Academy
Dai Wenrui, Shanghai Jiao Tong University
Xie Zhonglin, Peking University
Li Yuqing, Shanghai Jiao Tong University
4. Brain-inspired Neural Network Models, Algorithms and Applications (I,
II)
Organizers:
Zhou Dongzhuo, Shanghai Jiao Tong University
Li Songting, Shanghai Jiao Tong University
Speakers:
Li Guoqi, Institute of Automation, Chinese Academy of Sciences
Deng Lei, Tsinghua University
Zhang Tielin, Center for Brainsmatics and Intelligence, Chinese Academy of
Sciences
Yu Zhaofei, Peking University
Yu Shan, Institute of Automation, Chinese Academy of Sciences
Mi Yuanyuan, Tsinghua University
Du Kai, Institute for Artificial Intelligence, Peking University
Liu Chongming, Shanghai Jiao Tong University
5. Learning dynamical models from data: algorithms, analyses and
applications
Organizers:
Zhu Aiqing, National University of Singapore
Li Qianxiao, National University of Singapore
Speakers:
Lin Wei, Fudan University
Wu Kailiang, Southern University of Science and Technology
Xiong Shiying, Zhejiang University
Zhu Aiqing, National University of Singapore
6. Scientific Machine Learning
Organizers:
Lin Zhouhan, Shanghai Jiao Tong University
Speakers:
Bai Lei, Shanghai Artificial Intelligence Laboratory
Liu Pengfei, Shanghai Jiao Tong University
Lin Zhouhan, Shanghai Jiao Tong University
Zhang Quanshi, Shanghai Jiao Tong University
7. Understanding and Application of Large Models
Organizers:
Huang Weiran, Shanghai Jiao Tong University
Speakers:
Yuan Yang, Tsinghua University
Zhang Huishuai, Peking University
Gui Tao, Fudan University
Yi Mingyang, Huawei Noah's Ark Lab
8. Neural vs Symbolic
Organizers:
Hu Tianyang, Huawei Noah's Ark Lab
Speakers:
He Di, Peking University
Wang Wenjia, The Hong Kong University of Science and Technology (Guangzhou)
Qiu Yixuan, Shanghai University of Finance and Economics
Liu Zhengying, Huawei Noah's Ark Lab
9. Stochastic Models and Algorithms in Data Science (I, II)
Organizers:
Li Lei, Shanghai Jiao Tong University
Ling Shuyang, New York University Shanghai
Zhai Jiayu, ShanghaiTech University
Speakers:
Guo Ling, Shanghai Normal University
Chai Lihui, Sun Yat-sen University
Wu Hao, Tsinghua University
Zhou Xiang, City University of Hong Kong
Yu Haijun, Institute of Computational Mathematics and Scientific/Engineering
Computing, Chinese
Academy of Sciences
Cheng Cheng, Sun Yat-sen University
Zou Dongmian, Duke Kunshan University
10. Dynamical Equations and Machine Learning
Organizers:
Chen Jinrun, University of Science and Technology of China
Speakers:
Xiong Tao, Xiamen University
Wang Yanli, Beijing Computational Science Research Center
Wu Keke, Shanghai Jiao Tong University
Ma Zheng, Shanghai Jiao Tong University
11. Mechanism-driven Deep Learning Algorithms and Theory
Organizers:
Wang Dong, The Chinese University of Hong Kong (Shenzhen)
Liu Hao, Hong Kong Baptist University
Speakers:
Liu Hao, Hong Kong Baptist University
Wang Yating, Xi'an Jiaotong University
Xu Xinpeng, Guangzhou Technion - Israel Institute of Technology
Feng Han, City University of Hong Kong
12. Machine Learning Methods for Inverse Problems
Organizers:
Yan Liang, Southeast University
Speakers:
Jia Junxiong, Xi'an Jiaotong University
Wang Yuliang, Beijing Normal University
Zhang Yixuan, Zhejiang University
Zhong Min, Southeast University
13. Generative AI for Science (I, II)
Organizers:
Wang Han, Beijing Institute of Applied Physics and Computational Mathematics
Wang Lei, Institute of Physics, Chinese Academy of Sciences
Speakers:
Huang Wenbing, Renmin University of China
Xu Hongteng, Renmin University of China
Zhang Pan, Institute of Theoretical Physics, Chinese Academy of Sciences
Wu Tailin, Westlake University
Ke Guolin, Beijing Deepshape Technology Co., Ltd.
Lv Jian, Jilin University
Chen Pin, Sun Yat-sen University
Sun Jian, Xi'an Jiaotong University
14. AI for Imaging
Organizers:
Bao Chenglong, Tsinghua University
Liang Jingwei, Shanghai Jiao Tong University
Speakers:
Pang Tongyao, Tsinghua University
Niu Tianye, Shenzhen Bay Laboratory
Hu Zhanli, Shenzhen Institutes of Advanced Technology, Chinese Academy of
Sciences
Zhang Fa, Beijing Institute of Technology
15. Scientific Machine Learning Methods and Their Applications in
Computational
Mechanics
Organizers:
Huang Zhengyu, Peking University
Speakers:
Shi Zuoqiang, Tsinghua University
Sun He, Peking University
Meng Xuhui, Huazhong University of Science and Technology
Zhang Weiwei, Northwestern Polytechnical University
16. Efficient Algorithms and Analysis of Deep Learning for Solving
Complex Fluids (I,
II)
Organizers:
Mao Zhiping, Ningbo Eastern University of Science and Technology (tentative
name)
Speakers:
Wang Fei, Xi'an Jiaotong University
Hong Qizhen, Institute of Mechanics, Chinese Academy of Sciences
Gu Yiqi, University of Electronic Science and Technology of China
Xie Fangfang, Zhejiang University
Cai Shengze, Zhejiang University
Lin Chensen, Fudan University
Jin Pengzhan, Peking University
17. Machine Learning and Computational Chemistry (I, II)
Organizers:
Hu Wei, University of Science and Technology of China
Li Yingzhou, Fudan University
Speakers:
Chen Ji, Peking University
Wang Xiaonan, Tsinghua University
Lu Ziheng, Microsoft
Lyu Dingshun, ByteDance
Lan Zhenggang, South China Normal University
Jiang Jun, University of Science and Technology of China
Hu Sulei, University of Science and Technology of China
Chu Weibin, Fudan University
18. High-Performance Computing in Scientific Intelligence
Organizers:
Jia Weile, Institute of Computing Technology, Chinese Academy of Sciences
Speakers:
Li Shigang, Beijing University of Posts and Telecommunications
Yang Hailong, Beihang University
19. Applications of Machine Learning in Aerospace Dynamics (I,
II)
Organizers:
Zhang Tianhan, Southern University of Science and Technology
Speakers:
Wen Xin, Shanghai Jiao Tong University
Han Wang, Beihang University
An Jian, Northwestern Polytechnical University
Cai Zun, National University of Defense Technology
Han Xiao, Beihang University
Tian Ye, China Aerodynamics Research and Development Center
Chen Dongping, Beijing Institute of Technology
Xu Hui, Shanghai Jiao Tong University
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Venue Information
Venue: Chen Ruiqiu Building, Shanghai Jiao Tong University
Transportation, floor plans, and nearby information will be updated later.
Fee: No registration fee is required for the conference.
Accommodation: Participants should arrange accommodation by themselves.
Relevant Notifications
1. Conference Schedule
The conference will be held from August 7 to 9, 2026, at Chen Ruiqiu Building, Shanghai Jiao Tong University. August 7 is for registration, with conference sessions on the following two days. Four invited talks by young scholars will be arranged in the two mornings. A viewpoint forum will be arranged on the morning of the first day. Four rounds of parallel sessions will be arranged, with 7–8 sessions in each round. Each talk in the parallel sessions is 30 minutes. Around 30 parallel sessions and about 120 speakers are expected. The expected number of participants is 500–600.
2. Accommodation and Catering Arrangements
There is no registration fee. Accommodation should be arranged by participants themselves.
