黃海平

2022.04至今 中山大學教授

2018.03- 2022.04 中山大學百人計劃副教授

2014.08-2018.03 日本理化學研究所研究科學家（Research Scientist）

2012.08-2014.08 日本學術振興會外國人特別研究員（JSPS postdoctoral fellow）

2011.08-2012.08 香港科技大學訪問學者（Visiting Scholar）

2006.09-2011.07 中國科學院理論物理研究所博士生

2002.09-2006.06 中山大學理工學院物理學專業本科生 ^[1] 

理論物理學科：神經計算的統計物理學，具體的研究方向－

a. 無序系統的統計物理: 複本理論, 空腔方法, 物理啓發的消息傳遞算法，描述非線性動力學的動力學平均場理論;

b. 神經網絡的理論和計算模型: 監督學習神經網絡，受限玻爾茲曼機的平均場理論, 深度無監督學習, 循環神經網絡的平均場理論及其神經科學原理; 生物神經網絡的相變理論。 ^[1] 

(1) 中山大學百人計劃青年學術骨幹啓動經費（2018-2019）

(2) 國家青年科學基金項目：神經網絡無監督學習的相關統計物理研究 （2019-2021） ^[1] 

（3）國家優秀青年基金項目：神經網絡的統計物理（2022-2024）

[25] Y. Zhao, J. Qiu, M. Xie and H. Huang*, Equivalence between belief propagation instability and transition to replica symmetry breaking in perceptron learning systems, Phys. Rev. Research 4, 023023 (2022)

[24] J. Zhou, Z. Jiang, T. Hou, Z. Chen, KYM Wong and H. Huang*, Eigenvalue spectrum of neural networks with arbitrary Hebbian length, PHYSICAL REVIEW E 104, 064307 (2021). Side-by-Side paper

[23] Z. Jiang, J. Zhou, T. Hou, KYM Wong and H. Huang*, Associative memory model with arbitrary Hebbian length, PHYSICAL REVIEW E 104, 064306 (2021). Side-by-Side paper

[22] W. Zou and H. Huang*, Data-driven effective model shows a liquid-like deep learning, Phys. Rev. Research 3, 033290 (2021).

[21] J. Zhou and H. Huang*, Weakly correlated synapses promote dimension reduction in deep neural networks, Phys. Rev. E 103, 012315 (2021).

[20] C. Li and H. Huang*, Learning credit assignment, Phys. Rev. Lett. 125, 178301 (2020)

[19] H. Huang, Variational mean-field theory for training restricted Boltzmann machines with binary synapses, Phys. Rev. E 102, 030301(R) (2020) Rapid Communications

[18] T. Hou, and H. Huang*, Statistical physics of unsupervised learning with prior knowledge in neural networks, Phys. Rev. Lett. 124, 248302 （2020）.

[17] T. Hou, KYM Wong, and H. Huang*, Minimal model of permutation symmetry in unsupervised learning, J. Phys. A 52:414001 (2019) Invited Paper for the special issue of statistical physics and machine learning.

[16] H. Huang* and A. Goudarzi, Random active path model of deep neural networks with diluted binary synapses, PHYSICAL REVIEW E 98, 042311 (2018).

[15] H. Huang, Mechanisms of dimensionality reduction and decorrelation in deep neural networks, PHYSICAL REVIEW E 98, 062313 (2018). First theory model of linear dimensionality reduction.

[14] H. Huang, Role of zero synapses in unsupervised feature learning, 2018 J. Phys. A: Math. Theor. 51 08LT01. Published as a LETTER.

[13] H. Huang, Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses, J. Stat. Mech. (2017) 053302. Recommended in Quora.

[12] H. Huang, Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons, J. Stat. Mech. (2017) 033501.

[11] H. Huang* and T. Toyoizumi, Clustering of neural codewords revealed by a first-order phase transition, Phys. Rev. E 93, 062416 (2016). Selected as one of the most interesting and intriguing arXiv papers from the past week by MIT Technology Review.

[10] H. Huang* and T. Toyoizumi, Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition, Phys. Rev. E 94, 062310 (2016).

[9] H. Huang, Effects of hidden nodes on network structure inference, J. Phys. A: Math. Theor. 48 355002 (2015).

[8] H. Huang* and T. Toyoizumi, Advanced mean field theory of the restricted Boltzmann machine, Phys. Rev. E 91, 050101(R) (2015). Published as a Rapid Communication.

[7] H. Huang* and Y. Kabashima, Origin of the computational hardness for learning with binary synapses, Phys. Rev. E 90, 052813 (2014). Solved a long standing problem—why is a binary perceptron hard to learn

[6] H. Huang* and Y. Kabashima, Dynamics of asymmetric kinetic Ising systems revisited. J. Stat. Mech.: Theory Exp. P05020 (2014).

[5] H. Huang*, K. Y. Michael Wong and Y. Kabashima, Entropy landscape of solutions in the binary perceptron problem, J. Phys. A: Math. Theor. 46 375002 (2013). Selected in the Research Highlights section of J. Phys. A.

[4] H. Huang, Sparse Hopfield network reconstruction with L1 regularization. Eur. Phys. J. B 86, 484 (2013).

[3] H. Huang*, and Y. Kabashima, Adaptive Thouless-Anderson-Palmer approach to inverse Ising problems with quenched random fields. Phys. Rev. E 87, 062129 (2013).

[2] H. Huang* and H. Zhou, Counting solutions from finite samplings. Phys. Rev. E 85, 026118 (2012).

[1] H. Huang* and H. Zhou, Combined local search strategy for learning in networks of binary synapses. Europhysics Letters 96, 58003 (2011).

2012年，日本學術振興會外國人特別研究員（JSPS 博士後）

2017年，日本理化學研究所傑出研究獎 ^[1] 

Physical Review Letters, Physical Review X, Nature Communications, eLife, Physical Review E, Journal of Statistical Mechanics: Theory and Experiment, Journal of Physics A: Mathematical and Theoretical, Neural Networks, Eur. J. Phys. B, Physica A, Neurocomputing, PloS Comput Bio, Network Neuroscience 等十餘種國際專業雜誌的審稿人。 ^[1] 

數學與科學機器學習國際會議程序委員（主席） Program Committee of MSML22

第六屆全國統計物理與複雜系統學術會議大會報告

第15屆亞太物理會議邀請報告

德國於利希研究中心計算神經科學在線論壇邀請報告

在1915年創刊的《科學雜誌》（主編：白春禮 院士）撰寫 “統計物理、無序系統與神經網絡”