Ph.D. (Tsinghua University)

B.Sc. (Xi’an Jiaotong University)

Prof. 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) and Group Leader at the Frankfurt Institute for Advanced Studies (FIAS), leading the group “Deepthinkers” in AI for Science, especially Physics studies with modern computational paradigms Machine- and Deep-Learning. He also supervises Master/PhD students and Postdocs in AI for Science. He was then promoted to Fellow (W2 professor status) at FIAS from 2022. Since the end of 2023 he joined CUHK(SZ) as an Assistant Professor.

Prof. Zhou has broad interests in AI for Science, including high energy nuclear physics, statistical physics, energy, application oriented and other diverse fields. He remains committed to pushing the boundaries of physics and modern computational paradigms, especially machine learning, to provide new pathways and insights in exploring the physical world while also shaping the future of technological advances in computations with inspiration from physics knowledge.

Prof. Zhou has published more than 50 peer-reviewed journal papers. He has been invited to give plenary talks in International conferences like Strange Quark Matter, Initial Stages, International Nuclear Physics Conference, ML4Lattice, etc. He has been invited as reviewer for many journals, including: PRL, PRD, PRC, PRE, Nature Communications, Astrophysical Journal-AAS, JCAP, NST, CPC, Frontier of Physics, EPJA, Particles, Measurements, Sustainability, NeurIPS, PLOS ONE. He currently serves as associate editor for the journal Modern Physics Letters A and International Journal of Modern Physics A.

Google Scholar Link

Selected representative publications (* corresponding author(s)):

1. K. Zhou*, L. Wang, L. Pang, S. Shi, “Exploring QCD Matter in Extreme Conditions with Machine Learning", Prog. Part. Nucl. Phys. 104084 (2023) (Invited Review)
2. M. O.K, J. Steinheimer, K. Zhou*, H. Stoecker, “QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data", Phys. Rev. Lett. 131, 202303 (2023) (Editors’ Suggestion)
3. L. Wang, B. Hare, K. Zhou*, H. Stoecker, O. Scholten, “Identifying Lightning Structures via Machine Learning", Chaos, Solitons & Fractals 170, 113346 (2023)
4. S. Soma, L. Wang, S. Shi, H. Stoecker, K. Zhou*, “Reconstructing the Neutron Star Equation of State from Observational Data via Automatic Differentiation." Phys. Rev. D 107, 083028 (2023)
5. S. Chen, O. Savchuk, S. Zheng, B. Chen, H. Stoecker, L. Wang, K. Zhou*, “Fourier-Flow model generating Feynman Paths", Phys. Rev. D 107, 056001 (2023) 
6. S. Shi, L. Wang, K. Zhou*, “Rethinking the Ill-posedness of the Spectral Function Reconstruction – Why is it fundamentally hard and how Artificial Neural Networks can help”, Comput. Phys. Commun. 282, 108547(2023)
7. Y. Cheng, S. Shi, Y. Ma, H. Stoecker, K. Zhou*, “Examination of Nucleon Distribution with Bayesian Imaging for Isobar Collisions”, Phys. Rev. C 107, 064909 (2023)
8. Y. Zhao, L. Wang, K. Zhou*, X. Huang, "Detecting the Chiral Magnetic Effect via Deep Learning", Phys. Rev. C 106, L051901 (2022) (Letter)
9. L. Wang, S. Shi, K. Zhou*, "Reconstructing spectral functions via automatic differentiation", Phys. Rev. D 106, L051502 (2022) (Letter)
10. X. Gao, A. Hanlon, N. Karthik, S. Mukherjee, P. Petreczky, P. Scior, S. Shi, S. Syritsyn, Y. Zhao, K. Zhou, "Continuum-extrapolated NNLO valence PDF of the pion at the physical point", Phys. Rev. D 106, 114510 (2022)
11. L. Wang, Y. jiang, L. He, K. Zhou*, "Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition", Chinese Phys. Lett. 39, 120502 (2022)
12. S. Shi, K. Zhou*, J. Zhao, W. Mukherjee, P. Zhuang, "Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics", Phys. Rev. D 105, 014017 (2022)
13. Y. Sha, J. Faber, S. Gou, B. Liu, W. Li, S. Schramm, H. Stoecker, T. Steckenreiter, D. Vnucec, N. Wetzstein, A. Widl, K. Zhou*, "A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals", Engineering Applications of Artificial Intelligence 113, 104904 (2022)
14. S. Soma, L. Wang, S. Shi, H. Stoecker, K. Zhou*, "Neural Network Reconstruction of the Dense Matter Equation of State from Neutron Star Observables", JCAP 8, 071 (2022)
15. Y. Sha, J. Faber, S. Gou, B. Liu, W. Li, S. Schramm, H. Stoecker, T. Steckenreiter, D. Vnucec, N. Wetzstein, A. Widl, K. Zhou*, "Regional-Local Adversarially Learned One-Class Classifier Anomalous Sound Detection in Global Long-Term Space", KDD’22: Proceedings of the 28^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
16. M. O.K, K. Zhou*, J. Steinheimer, A. Redelbach, H. Stoecker, "An equation-of-state-meter for CBM using PointNet", JHEP 10, 184 (2021)
17. L. Wang, T. Xu, T. Stoecker, H. Stoecker, Y. Jiang, K. Zhou*, "Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID19-risk", Machine Learning: Science and Technology 2, 035031 (2021)
18. L. Jiang, L. Wang, K. Zhou*, "Deep Learning Stochastic Processes with QCD Phase Transition", Phys. Rev. D 103, 116023 (2021)
19. M. O. K, J. Steinheimer, K. Zhou*, A. Redelbach, H. Stoecker, "A fast centrality-meter for heavy-ion collisions at the CBM experiment", Phys. Lett. B 811, 135872 (2020)
20. Y. Du, K. Zhou*, J. Steinheimer, L. Pang, A. Motornenko, H. Zong, X. Wang, H. Stoecker, "Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning", Eur. Phys. J. C 80, 516 (2020)
21. J. Steinheimer, L. Pang, K. Zhou, V. Koch, J. Randrup, H. Stoecker, "A machine learning study to identify spinodal clumping in high energy nuclear collisions", JHEP 12, 122 (2019)
22. K. Zhou*, G. Endroedi, L. Pang, H. Stoecker, "Regressive and generative neural networks for scalar field theory", Phys. Rev. D 100, 011501 (2019) (Rapid Communication) [102 citations]
23. J. Zhao, K. Zhou, S. Chen, P. Zhuang, "Heavy flavors under extreme conditions in high energy nuclear collisions", Prog. Part. Nucl. Phys. 114, 103801 (2020) (Invited Review)
24. L. Pang, K. Zhou*, N. Su, H. Petersen, H. Stoecker and X. Wang, “An equation-of-state-meter of quantum chromodynamics transition from deep learning”,  Nature Commun. 9 (2018) no.1, 210 [153 citations]
25. K. Zhou, Z. Xu, P. Zhuang, C. Greiner, "Kinetic description of Bose-Einstein condensation with test particle simulations", Phys. Rev. D 96, 014020 (2017)
26. K. Zhou*, Z. Chen, C. Greiner, P. Zhuang, "Thermal Charm and Charmonium Production in Quark Gluon Plasma", Phys. Lett. B 758, 434 (2016)
27. Z. Xu, K. Zhou, P. Zhuang, C. Greiner, "Thermalization of gluons with Bose-Einstein condensation", Phys. Rev. Lett. 114, 182301 (2015)
28. K. Zhou, N. Xu, Z. Xu, P. Zhuang, "Medium effects on charmonium production at ultra-relativistic energies available at the CERN Large Hadron Collider", Phys. Rev. C 89, 054911 (2014) [256 citations]