职称：副教授
办公室：冼为坚堂214
学位：理学博士
毕业学校：中国科学院理论物理研究所
邮 ?箱：huanghp7@mail.sysu.edu.cn
实验室主页:?https://www.labxing.com/hphuang2018
ORCID page:?http://orcid.org/0000-0001-8757-4733
其他主页：https://www.researchgate.net/profile/Haiping_Huang;
?https://sites.google.com/site/physhuang?
“Pay Attention To Research Itself, rather than the order of the authors or the journal title; We are doing research just for FUN"

主要经历:
2018.03- 中山大学****副教授
2014.08-2018.03 日本理化学研究所研究科学家（Research Scientist)
2012.08-2014.08 日本学术振兴会外国人特别研究员（JSPS postdoctoral fellow）
2011.08-2012.08 香港科技大学访问****（Visiting Scholar）
2006.09-2011.07 中国科学院理论物理研究所博士生
2002.09-2006.06 中山大学理工学院物理学专业本科生

学科方向:
理论物理学科：神经计算的统计物理学，具体的研究方向－
a. 无序系统的统计物理: 复本理论, 空腔方法，描述非线性动力学的动力学平均场理论;
b. 神经网络的理论和计算模型: 监督学习神经网络，受限玻尔兹曼机的平均场理论, 深度无监督学习, 循环神经网络的平均场理论及其神经科学原理; 生物神经网络的相变理论。
??????? 本人在感知学习模型的解空间结构, 深度神经网络的降维理论, 视网膜神经网络的相变理论, 无监督学习的内禀对称性破缺等方向取得了国际同行公认的学术成果。?标志性的成果有如下四项: (1)离散感知神经网络解空间结构的阐明, 解决了长期困扰神经网络理论学界的关于离散感知机计算困难性起源的问题 (Phys Rev E, 2014)。(2)无监督学习的物理本质 (Phys Rev E, Rapid Communication, 2015，Phys Rev E 2016, JSTAT 2017, JPA 2018, 2019, PRL 2020)。 (3)一级相变揭示了神经编码结构的团簇性质, 理论上阐明了全静息码(所有神经细胞短暂不发放)的局域团簇结构逼近理论极限 (Phys Rev E, 2016)。(4)发现了多层神经网络的降维机制 （Phys Rev E， 2018）。
??????? 欢迎报考硕士研究生或申请博士后或专职科研人员；也欢迎高年级本科生加入课题组。
??????? The research group focuses on theoretical bases of various kinds of neural computation, including associative neural networks, restricted Boltzmann Machines, recurrent neural networks, and their deep variants. We are aslo interested in developing theory-grounded algorithms for real-world applications, and relating the theoretical study to neural mechanisms. Our long-term goal is to uncover basic principles of machine/brain intelligence using physics-based approximations.

承担课题:
(1) 中山大学****青年学术骨干启动经费（2018-2019）
(2) 国家青年科学基金项目：神经网络无监督学习的相关统计物理研究 （2019-2021）

荣誉获奖:
2012年，日本学术振兴会外国人特别研究员（JSPS 博士后）
2017年，日本理化学研究所杰出研究奖

主要兼职:
Physical Review Letters, Nature Communication,?Physical Review X, Scientific Reports, 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 等十余种国际专业杂志的审稿人。?

代表论著:
*: corresponding author
Our lab has published around 30 papers (a full list at the lab homepage or ORCID page) covering?all main statistical physics journals, including PRL, PRE, JPA, EPL, JSTAT, EPJB, JSP etc.
[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).?