Professor Kyle Cranmer, University of Wisconsin-Madison 

Kyle Cranmer is the David R. Anderson Director of the UW-Madison Data Science Institute and a Professor of Physics with courtesy appointments in Statistics and Computer Science. He is also the Editor-in-Chief of the journal Machine Learning Science and Technology. Cranmer was a Professor of Physics and Data Science at NYU from 2007 – 2022. He obtained his BA in Physics and Mathematics at Rice University in 1999 and his Ph.D. in Physics from the University of Wisconsin-Madison in 2005. He was awarded the Presidential Early Career Award for Science and Engineering in 2007, the National Science Foundation’s Career Award in 2009, and became a Fellow of the American Physical Society in 2021 for his work at the Large Hadron Collider. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. His current interests are at the intersection of physics, statistics, and machine learning.

Professor Weinan E, Center for Machine Learning Research, Peking University

Weinan E is a member of the Chinese Academy of Sciences, a professor in the Center for Machine Learning Research (CMLR) and the School of Mathematical Sciences at Peking University. He is also a professor at the Department of Mathematics and Program in Applied and Computational Mathematics at Princeton University. His main research interest is numerical algorithms, machine learning and multi-scale modeling, with applications to chemistry, material sciences and fluid mechanics. 

Professor Xingao Gong, Fudan University

Xingao Gong is a member of the Chinese Academy of Sciences, a professor at Fudan University, condensed matter physicist, and director of the Key Laboratory of Computational Materials Science under the Ministry of Education. Primarily engaged in research on condensed matter system structures, electronic structures, and the development of computational methods.

Professor Hideki Okawa, Institute of High Energy Physics, Chinese Academy of Sciences 

Hideki Okawa is a professor at the Institute of High Energy Physics (IHEP), Chinese Academy of Sciences. He obtained his bachelor and PhD at the University of Tokyo in 2004 and 2010 respectively. Between 2010-2014, he worked as a postdoctoral scholar at University of California, Irvine and then at Brookhaven National Laboratory. In 2014, he joined University of Tsukuba as an international tenure-track assistant professor and was tenured in 2018. In 2019, he joined Fudan University as a University Key Position Professor. He moved to IHEP in 2022. His main research area is TeV-scale collider experiments (LHC-ATLAS, CEPC), machine learning and quantum computing applications.

Professor Dong-Ling Deng, Tsinghua University

Dong-Ling Deng is a tenured associate professor at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University. He earned his Ph.D. degree from the University of Michigan and was the recipient of the Kent M. Terwilliger Memorial Thesis Prize (2015). Following a JQI (Joint Quantum Institute) postdoctoral fellowship, he joined IIIS, Tsinghua University as an assistant professor in 2018. He is the recipient of the National Science Fund for Distinguished Young Scholars. Prof. Deng’s research group focuses primarily on quantum artificial intelligence, encompassing quantum enhanced AI as well as AI applications for quantum physics.

Professor Berend Smit, EPFL

Berend Smit is a professor at EPFL specializing in molecular simulations for energy applications. He earned MSc degrees in Chemical Engineering and Physics from TU Delft and a PhD in Chemistry from Utrecht University. He previously held positions at Shell Research, the University of Amsterdam, and UC Berkeley, where he was also a Faculty Chemist at LBNL. He served as Director of CECAM in Lyon and was elected a Foreign Member of the Royal Netherlands Academy of Arts and Sciences (KNAW) in 2024. He co-authored Understanding Molecular Simulations and Introduction to Carbon Capture and Sequestration.