博士（清华大学）

学士（西安交通大学）

周凯教授于2009年在西安交通大学物理系获得理学学士学位，2014年在清华大学物理系获得博士学位。此后，他于2014至2017年在德国法兰克福歌德大学理论物理研究所（ITP）担任博士后研究员。自2017年7月起，他在法兰克福高等研究院（FIAS）担任研究员（W1教授级别）及团队负责人，领导“Deepthinkers”小组进行AI for Science研究项目，特别是在现代计算范式下的高能核物理研究，包括应用机器和深度学习，并指导硕士/博士学生和博士后在人工智能科学方面的研究。2022年，他在FIAS晋升为高级研究员（W2教授级别）。自2023年底，他加入香港中文大学（深圳）理工学院担任助理教授。

周教授在AI for Science领域拥有广泛的兴趣，包括高能核物理、统计物理、能源、应用导向以及其他多元化领域。他致力于推动融合物理学研究与现代计算范式如人工智能，特别是机器学习，以提供新的途径和洞察力来探索物理世界，同时也以物理学知识为灵感，以塑造计算技术的进一步发展。

他已发表50多篇同行评审期刊论文和众多会议论文，并曾受邀在多个国际会议作大会报告，如SQM, Initial Stages，国际核物理大会，机器学习格点场论等。他也受邀为多家期刊审稿，包括：PRL、PRD、PRC、PRE、Nature Communications、Astrophysical Journal-AAS、JCAP、NST、CPC、Frontier of Physics、EPJA、Particles、Measurements、Sustainability、NeurIPS、PLOS ONE等。目前担任《现代物理通讯A》和《国际现代物理A》期刊的副主编。

谷歌学术链接

代表性论文（*通讯作者）：

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]