Physics, Machine and Intelligence

Haiping Huang

Professor

Lab for Physics, Machine and Intelligence

Sun Yat-sen University, Guangzhou, China

Email: physhuang [AT] gmail [DOT] com; huanghp7[AT]mail.sysu.edu.cn

Research Interests

Statistical Physics, Artificial Intelligence, and Theoretical Neuroscience.

Research is not for publication, but for solving hard problems; we are interested in defining and solving hard problems of intelligence: the nature of perception, memory, and imagination.

I am now at Sun Yat-sen University to start a research group at the interplay between physics, brain science, and machine learning. Welcome to contact me if you want to visit us, or join us as a master/PhD student or research fellow.

课题组公众号PMI Lab见此链接，内含科普、讲课视频、实验室前沿论文介绍等。

I have published one monograph as follows:

Haiping Huang, Statistical Mechanics of Neural Networks (Springer, Singapore, 2022)

and a few general introductions to the field:

Haiping Huang, Statistical physics, disordered systems and neural networks , KEXUE (since 1915) 74 (1), 40-44 (2022)

Haiping Huang, Physics underlying neural network theory, Modern Physics 36 (6): 49 (2024)

Ruoran Bai, Liru Zhang, Haiping Huang, 2024 Nobel physics and related interdisciplinary studies, Physics 54 (1) (2025)

and one perspective article:

H. Huang, Eight challenges in developing theory of intelligence, Front. Comput. Neurosci.18:1388166 (2024).

Representative papers in PMI lab

If you want to join PMI lab, please make sure that you can understand/reproduce at least one of the following papers; their impacts are introduced in Achievements.

1. J. Phys. A: Math. Theor. 46 375002 (2013)

2. Phys. Rev. E 90, 052813 (2014)

3. Phys Rev E 98, 062313 (2018).

4. J. Phys. A: Math. Theor. 52, 414001 (2019).

5. Phys. Rev. Lett. 124, 248302 (2020).

6. Phys. Rev. E 103, 012315 (2021).

7. Phys. Rev. E 108, 014309 (2023).

8. Phys. Rev. Research 5, 013090 (2023).

9. Sci. China-Phys. Mech. Astron. 68, 210511 (2025).

10. Phys. Rev. E 111, 014111 (2025).

11. Commun. Theor. Phys. 77 035601 (2025).

12. Phys. Rev. E 112, L013301 (2025) .

 Research News

• • I was recently awarded by APS:  Physical Review E Reviewer Excellence Award in 2026

  • My Chinese monograph 《物理、机器与智能：从复杂到简单》 has been submitted to Shanghai SciTech Press for publication, supported by the NNSFC Theoretical Physics Special Fund.

  • Our manuscript was recently accepted by Phys Rev E, see the link here. It determines the scaling behavior of kinetic energy (how fast the dynamics is) at the edge of chaos; meanwhile, it reports an error in Sompolinsky's previous classic paper (PRL 88', PRE 18')

Education and research experience

• 2022.04-now Professor, Sun Yat-sen University

• 2018.05–2022. 04 Associate Professor, Sun Yat-sen University

• 2014.08–2018.05 Research Scientist, RIKEN Brain Science Institute, Saitama, Japan.

Lab for neural computation and adaptation, in collaboration with Taro Toyoizumi.

• 2012.08–2014.08 JSPS Postdoctoral Fellow, Department of Computational Intelligence and Systems

Science, Tokyo Institute of Technology, Yokohama, Japan.

Statistical mechanical approach to massive probabilistic inference, in collaboration with

Yoshiyuki Kabashima.

• 2011.08–2012.08 Visiting Scholar, Department of Physics, The Hong Kong University of Science and

Technology, Clear Water Bay, Hong Kong, in collaboration with K Y Michael Wong.

• 2006.09-2011.07 PhD, Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China. Applications of Statistical Physics to Graphical Models of Statistical Inference, supervised by Haijun Zhou.

• 2002.09-2006.06 Bachelor of Science, Physics, Sun Yat-sen University, Guangzhou, China.

Honors and awards

Aug 2026  Physical Review E Reviewer Excellence Award

Aug 2021  Excellent Young Scientist Fund, China

Mar 2017  8th RIKEN Research Incentive Award, RIKEN

Jan 2012 JSPS Postdoctoral Fellowship for Foreign Researchers, Japan Society for the Promotion of Science (JSPS)

Courses

Undergraduate: General physics, Thermodynamics and statistical physics, Complex systems and nonlinear physics

Graduate: Statistical mechanics of neural networks

Students supervised

Undergraduate (excellent bachelor's thesis): Chan Li, Ziming Chen, Yuhao Li

Master: Jianwen Zhou (CAS), Chan Li (UCSD), Wenxuan Zou (Duke), Zijian Jiang (Princeton), Minshan Xie (TJU), Junbin Qiu (HKUST GZ), Zhendong Yu (PKU), Weizhong Huang

Phd: Wenkang Du

LAB Members

Undergraduate: Zizuo Qi, Xinrui Wen

Master: Ruoran Bai (2024), Shishe Wang (2024), Mengyao An (2025), Xinyu Gao (2026)

Phd: Liru Zhang (2023), Binhai Qin (2024), Tianmin Fang (2025), Chenhui Pan (2026)

Referee Services

Physical Review Letters, Nature Machine Intelligence, Nature Communications, Physical Review X, eLife, PRX Life, National Science Review, SciPost Phys, Chin. Phys. Lett, PLoS Computational Biology, Network Neuroscience, Physical Review E, Physical Review Research, Machine Learning: Science and Technology, Chaos, Entropy, Phys Rev B, J. Stat. Mech, J. Phys. A, Eur. Phys. J. B, Chin. Phys. B, J. Stat. Phys, Communications in Theoretical Physics, Neural Networks, Neurocomputing, Physica A, Scientific Reports, Frontiers in Computational Neuroscience, IEEE Transactions on Neural Networks and Learning Systems