The China conference on Scientific Machine Learning
CSML 2024
The conference aims to delve into three key areas of machine learning. Firstly, mathematical machine learning, where we are committed to exploring the theoretical foundations and model construction of machine learning from a mathematical perspective. Secondly, scientific machine learning, where we will discuss how machine learning can be applied to solve complex scientific problems, as well as how to draw inspiration from scientific computing to design innovative machine learning algorithms. Lastly, industrial machine learning, where we will examine how to apply the findings of machine learning research to industrial production, in order to drive technological advancement and efficiency improvements in the industry.
Organization of the Conference
Conference Chairs (in alphabetical order by surname)
|
E Weinan |
(Academician, Peking University, Beijing Big Data Research Institute) |
|
Jin Shi |
(Professor, Shanghai Jiao Tong University, Institute of Natural Sciences) |
Conference Academic Committee (in alphabetical order by surname)
|
Bao Gang |
(Academician, Zhejiang University) |
|
E Weinan |
(Academician, Princeton University, Beijing Big Data Research Institute) |
|
Gao Xiaoshan |
(Researcher, Academy of Mathematics and Systems Science, Chinese Academy of Sciences) |
|
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) |
|
Wang Liwei |
(Professor, Peking University) |
|
Yang Zhijian |
(Professor, Wuhan University) |
|
Yin Wotao |
(Alibaba (USA) DAMO Academy, Decision Intelligence Lab) |
|
Zhang Bo |
(Academician, Tsinghua University) |
|
Zhang Pingwen |
(Academician, Peking University) |
|
Zhang Zhihua |
(Professor, Peking University) |
|
Zhou Zhihua |
(Professor, Nanjing University) |
Conference Organizing Committee (in alphabetical order by surname)
|
Dong Bin |
(Peking University) |
|
Luo Tao |
(Shanghai Jiao Tong University) |
|
Jiao Yuling |
(Wuhan University) |
|
Ma Zheng |
(Shanghai Jiao Tong University) |
|
Xu Zhiqin |
(Shanghai Jiao Tong University) |
|
Zhang Yaoyu |
(Shanghai Jiao Tong University) |
Conference Schedule
| Day 1 | |
|---|---|
| 8:20-8:40 | Opening Remarks |
| 8:40-9:25 | Invited Speaker |
| 9:30-10:15 | Invited Speaker |
| 10:15-10:30 | Coffee Break |
| 10:30-12:00 | Viewpoint Forum |
| 12:00 | Group Photo |
| 13:30-15:30 | Parallel Sessions |
| 15:30-16:00 | Coffee Break |
| 16:00-18:00 | Parallel Sessions |
| Day 2 | |
| 8:30-9:15 | Invited Speaker |
| 9:15-10:00 | Invited Speaker |
| 10:00-10:15 | Coffee Break |
| 10:15-12:15 | Parallel Sessions |
| 13:30-15:30 | Parallel Sessions |
| 15:40-16:00 | Closing Ceremony |
Session Themes
Session Themes
2. Recent Advances in Machine Learning Theory (I, II)
3. Recent Advances on Learning to Optimize
4. Brain-inspired Neural Network Models, Algorithms, and Applications (I, II)
5. Learning dynamical models from data: algorithms, analyses and applications
6. Scientific Machine Learning
7. Understanding and Application of Large Models
9. Stochastic Models and Algorithms in Data Science (I, II)
10. Dynamical Equations and Machine Learning
11. Mechanism-driven Deep Learning Algorithms and Theory
12. Machine Learning Methods for Inverse Problems
13. Generative AI for Science (I, II)
15. Scientific Machine Learning Methods and Their Applications in Computational Mechanics
16. Efficient Algorithms and Analysis of Deep Learning for Solving Complex Fluids (I, II)
17. Machine Learning and Computational Chemistry (I, II)
18. High-Performance Computing in Scientific Intelligence
19. Applications of Machine Learning in Aerospace Dynamics (I, II)
Session Information
Symposium Agenda in the Field of Machine Learning and Artificial Intelligence
1. Machine Learning Theory
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
Location of the Conference
Location: Shanghai Jiao Tong University
Past conferences
Links to past conferences
2022 Annual Machine Learning and Scientific Applications Conference
Important Dates
Conference Dates: 2024.8.10-2024.8.11
Registration Notice
Fee: No registration fee is required for the conference.
Relevant Notifications
1. Conference Scale
In the two mornings, four young scholars will be invited to give talks. On the first morning, there will be a viewpoint forum. A total of 26 parallel sessions will be held over two days, divided into four rounds, with 6-7 sessions in each round. Each session lasts 2 hours, typically with 4 talks, each lasting 30 minutes. There will be around 200 conference participants.
2. Venue and Accommodation Arrangements
Speakers & organizers will be accommodated at the Platinumhan Jue Hotel by default, at a rate of 550 yuan per day (including single breakfast).
Contact Info
TBD