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Abstract:
This talk will focus on AI meets High Energy Nuclear Physics from inverse problem solving and deep generative modelling, to introduce the methodologies of machine learning used in exploring QCD matter under extreme conditions. Related to the inverse problem solving for QCD matter studies, supervised learning, Bayesian Inference and automatic differentiation based discriminative AI methods will be discussed. For Deep Generative modelling, the flow-based models and diffusion models will be discussed for their usage in lattice field theory and heavy ion collisions simulations.
Biography:
Dr. Kai Zhou received his B.Sc. degree in Physics from Xi'an Jiaotong University in 2009, and his PhD degree in Physics from Tsinghua University in 2014. After that, he worked as a Postdoctoral researcher in the Institute for Theoretical Physics (ITP) at Goethe University Frankfurt in Germany from 2014 to 2017. Since 2017 he started as a Research Fellow (W1 professor status) Group Leader at the Frankfurt Institute for Advanced Studies (FIAS), leading the AI for Science group “Deepthinkers”, focusing on physics studies with modern computational paradigms Machine- and Deep-Learning, supervises Master/PhD students and Postdocs in AI for Science. From 2022 he was then promoted to Fellow (W2 status) at FIAS. He joined CUHK-Shenzhen as an Assistant Professor since the end of 2023.
Host: Prof. Wei Wang
Alternative online link：https://meeting.tencent.com/dm/H6qHuHXTVPMm
ID: 848901011 Password: 123456