For the first time in the history of ICML, professors from Chinese mainland universities have been appointed as directors. Lin Zhouchen from Peking University and Yan Junchi from Shanghai Jiao Tong University have been elected.
The election results for the ICML 2025 Board of Directors are officially out!
This year, a total of five scholars have been elected to the board. Among them, two well - known scholars from universities in mainland China - Professor Zhouchen Lin from Peking University and Professor Junchi Yan from Shanghai Jiao Tong University - have both been elected.
This is the first time that scholars from universities in mainland China have been elected to the ICML Board of Directors.
Recently, ICML 2025 concluded successfully at the Vancouver Convention Centre in Canada.
This year, a total of 12,107 valid submissions (excluding "desk - rejected" papers) were received, representing a 28% increase compared to the total in 2024. Among them, 3,260 papers were accepted, with an acceptance rate of 26.9%.
ICML, short for the International Conference on Machine Learning, is a top - tier professional conference dedicated to promoting the development of machine learning, a branch of AI.
In the fields of computer engineering and science, ICML is recognized as one of the three top AI conferences.
As one of the fastest - growing artificial intelligence conferences globally, ICML brings together participants with diverse backgrounds, including researchers, entrepreneurs, engineers, postgraduate students, and post - docs from both academia and industry.
In 2014, the ICML conference was held in Beijing.
Let's take a look at all the members of the ICML Board of Directors elected this year.
For the first time! Professors from universities in mainland China serve as directors of ICML 2025
Zhouchen Lin
Zhouchen Lin is currently the Deputy Dean of the School of Artificial Intelligence at Peking University and a Boya Distinguished Professor. He is a fellow of IAPR, IEEE, CSIG, and AAIA, a National Outstanding Youth Fund recipient, the former director of the Machine Vision Special Committee of the China Society of Image and Graphics, the deputy director of the Pattern Recognition and Machine Intelligence Special Committee of the Chinese Association of Automation, a standing member of the Computer Vision Special Committee of the China Computer Federation, and a standing member of the Pattern Recognition Special Committee of the Chinese Association for Artificial Intelligence.
His research areas include machine learning, computer vision, and numerical optimization.
He has published over 350 papers and five monographs in Chinese and English. His papers have been cited over 40,000 times on Google Scholar, with an h - index of 86. He holds 46 US patents and 18 Chinese patents.
He has also served as a senior area chair and area chair for many conferences, including CVPR, ICCV, NIPS/NeurIPS, ICML, IJCAI, AAAI, and ICLR. He was an editorial board member of IEEE T. PAMI, the co - program chair of ICPR 2022, and is currently an editorial board member of IJCV, Optimization Methods and Software, and Acta Automatica Sinica.
Professor Zhouchen Lin graduated from Nankai University for his undergraduate studies. He then obtained master's degrees from Peking University and the Hong Kong Polytechnic University, and a doctorate in 2000.
Junchi Yan
Junchi Yan is a professor at the School of Artificial Intelligence at Shanghai Jiao Tong University. He is a fellow of IAPR and the principal investigator of major projects of the Ministry of Science and Technology, major research plan projects of the National Natural Science Foundation, and outstanding young fund projects.
He worked as a researcher at IBM and a senior consultant at Amazon for a total of 10 years. His main research interests are machine learning and its cutting - edge applications.
In addition, Junchi Yan has served as a (senior) area chair for conferences such as CVPR, ICML, NeurIPS, and ICLR, and an editorial board member of IEEE TPAMI. He has also won the First Prize of Provincial and Ministerial Natural Science, been short - listed for the Best Paper Award at CVPR24, and received honors such as IEEE - CS AI'10 to Watch.
He graduated from the School of Automation at the University of Science and Technology Beijing for his undergraduate studies, and obtained a master's degree in Pattern Recognition and Intelligent Systems and a doctorate in Information and Communication Engineering from Shanghai Jiao Tong University.
In addition to the above two professors from universities in mainland China being elected to the board, three other scholars have also made the list.
Gautam Kamath
Gautam Kamath is currently an assistant professor at the Cheriton School of Computer Science at the University of Waterloo and a researcher at the Vector Institute. He holds the title of CIFAR AI Chair Professor in Canada.
His research focuses on trustworthy statistics and machine learning, with a particular emphasis on key issues such as data privacy and algorithmic robustness.
In May 2012, he graduated from Cornell University with dual degrees in computer science and electrical and computer engineering.
After that, he pursued a doctorate in the Theory of Computation Group at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and graduated in 2018.
Andreas Krause
Andreas Krause is a professor of computer science at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), leading the Learning and Adaptive Systems Group. He also serves as the academic co - director of the Swiss Data Science Center, the chair of the ETH AI Center, and co - founded the ETH spin - off company LatticeFlow.
Previously, he was an assistant professor of computer science at the California Institute of Technology.
He obtained his Ph.D. in computer science from Carnegie Mellon University in 2008 and a Diplom degree in computer science and mathematics from the Technical University of Munich in 2004.
Andreas is a fellow of ACM, IEEE, ELLIS, and a Kavli Frontiers Scholar of the US National Academy of Sciences.
He also received the Test of Time Award from ACM SIGKDD in 2019 and ICML in 2020.
He served as the co - program chair of ICML in 2018 and the conference chair of ICML in 2023. From 2023 to 2024, he was a member of the United Nations High - level Advisory Board on Artificial Intelligence.
Masashi Sugiyama
Masashi Sugiyama is the director of the RIKEN Center for Advanced Intelligence Project and a professor at the University of Tokyo.
He has served as the program chair of ACML 2010, NeurIPS 2015, AISTATS 2019, and ACML 2020.
Masashi Sugiyama was born in Osaka, Japan in 1974. He obtained his bachelor's, master's, and doctoral degrees in computer science from the Tokyo Institute of Technology in 1997, 1999, and 2001 respectively.
In 2001, he became an assistant at the Tokyo Institute of Technology, was promoted to associate professor in 2003, and transferred to the University of Tokyo as a professor in 2014. Since 2016, he has also served as the director of the RIKEN Center for Advanced Intelligence Project, leading research on basic AI technologies, application development, and social impacts.
His research areas cover machine learning theory and statistical data analysis. He has written several monographs on machine learning, such as "Introduction to Statistical Machine Learning".
A review of previous Chinese directors
According to the official ICML website, there are three other Chinese scholars among the previous directors: Eric Xing, Le Song, and Tong Zhang.
Eric Xing
Currently, Professor Eric Xing is a professor of computer science at Carnegie Mellon University and the President of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
He graduated from Tsinghua University for his undergraduate studies and obtained a doctorate in molecular biology and biochemistry from Rutgers, The State University of New Jersey, and a doctorate in computer science from the University of California, Berkeley.
His main research areas cover the innovation of machine learning and statistical methods, as well as the development of large - scale computing systems and architectures, aiming to solve problems related to automated learning, reasoning, and decision - making in artificial, biological, and social systems involving high - dimensional, multi - modal, and dynamic possible worlds.
