张超 (副教授)

副教授   博士生导师   硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:数学科学学院

学科:计算数学

办公地点:创新园#A1024

联系方式:0411-84708351

电子邮箱:chao.zhang@dlut.edu.cn

个人简介

主要研究方向:机器学习、深度学习、随机矩阵、统计学习理论、人工智能基础数学理论、大型装备制造智能运维和数字孪生等,其相关研究工作发表于权威期刊:IEEE-TIT、IEEE-TNNLS、JMLR、Neural Computation、MSSP等;权威会议:NIPS、AISTATS、UAI。主持国家自然科学基金青年项目1项、面上项目2项、国家重点研发计划课题1项。


一、科研项目

[主持项目]

  1. 多任务学习的理论分析与应用,国家自然科学基金青年项目,No. 11401076,2015-01-01至2017-12-31,总项目经费22万元;                                                             

  2. 基于非独立同分布样本的统计学习理论研究与应用,国家自然科学基金面上项目,No. 61473328,2015-01-01至2018-12-31,总项目经费58万元;                          

  3. 构建适用于多行业的大数据分析平台,大连市创新人才培育计划青年科技之星项目,No. ZX20160392, 大连市科学技术局, 2016-01-01至2016-12-31,总项目经费10万元;

  4. 国家重点研发计划“网络协同制造和智能工厂”重点专项项目“钢铁全流程多工序动态协同运行优化技术及示范应用”-课题4:“基于MR和大数据智能的产品质量缺陷溯源与在线工艺优化”,No. 2020YFB1711104,2020年11月-2023年10月,总项目经费204万。

  5. 国家自然科学基金面上项目, 面向深度神经网络的统计学习理论研究, No. 62176040, 2022-01至2025-12,项目直接经费57万元。

[参与项目]

  1. 国家重点研发计划“网络协同制造和智能工厂”重点专项项目“互联网+”产品定制设计方法与技术-课题“多源数据驱动的定制产品用户体验与性能预测技术”,总项目经费270万,2018YFB1700704,中华人民共和国技术部,2019年1月-2022年12月。

  2. 国家自然科学基金-天元专项,Kirchhoff/Cosserat弹性杆在不光滑曲面约束下的动力学问题,总项目经费3万元, No. 11126056, 2012年01月01日至2012年12月31日



二、学术论文

[2022]

  1. Chao Zhang, Minming Liang, Xueguan Song, Lixue Liu, Hao Wang, Wensheng Li and Maolin Shi, Generative adversarial network for geological prediction based on TBM operational data, Mechanical Systems and Signal Processing, vol. 162, no. 1, pp. 108035, January 2022. 

[2021]

  1. Liye Lv, Chaoyong Zong, Chao Zhang and Xueguan Song, Multi-Fidelity Surrogate Model Based on Canonical Correlation Analysis and Least Squares, Journal of Mechanical Design, vol. 143, no. 2, pp. 021705, 2021. 

  2. Yueqi Xu, Xueguan Song, Chao Zhang*, Hierarchical Regression Framework for Multi-fidelity Modeling, Knowledge-Based Systems, vol. 212, pp. 106587, 2021.

  3. Chao Zhang, Dacheng Tao, Tao Hu and Bingchen Liu, Generalization Bounds of Multi-task Learning from Perspective of Vector-valued Function Learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 1906 - 1919, 2021.

  4. Xiaonan Lai, Shuo Wang, Zhenggang Guo, Chao Zhang, Wei Sun and Xueguan Song, Designing a shape-performance integrated digital twin based on multiple models and dynamic data: a boom crane example, Journal of Mechanical Design, vol. 143, no. 7, pp: 071703 (14 pages), Jul 2021. 

  5. Fengjie Zheng, Chaoyong Zong, Chao Zhang, Xueguan Song, Fuzheng Qu, William Dempster, Dynamic Instability Analysis of a Spring-Loaded Pressure Safety Valve Connected to a Pipe by Using Computational Fluid Dynamics Methods, J. Pressure Vessel Technol., vol. 143, no. 4, pp: 041403 (14 pages), Aug 2021.

  6. Zhenhao Jiang, Tingting Pan, Chao Zhang and Jie Yang, A New Oversampling Method Based on the Classification Contribution Degree, Symmetry, vol. 13, pp. 194, Jan 2021. 

  7. Xianjie Gao, Chao Zhang* and Hongwei Zhang, Dimension-free bounds for largest singular values of matrix Gaussian series, Communications in Statistics - Theory and Methods, vol. 50, no. 10, pp. 2419-2428, 2021. 

  8. Zhenhao Jiang, Tingting Pan, Chao Zhang and Jie Yang, A New Oversampling Method Based on the Classification Contribution Degree, Symmetry, vol. 13, no. 2, pp. 194, 2021. 

[2020]

  1. Chao Zhang, Xianjie Gao, Min-Hsiu Hsieh, Hanyuan Hang and Dacheng Tao, Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications, IEEE Transactions on Information Theory,vol. 66, no. 2, pp. 1099 - 1117, 2020. 

  2. Xue Xu, Kuo Yang, Feilong Zhang, Wenwen Liu, Yinyan Wang, Changying Yue, Junyao Wang, Keke Zhang, Chao Zhang, Goran Nenadic, Dacheng Tao, Xuezhong Zhou, Hongcai Shang and Jianxin Chen, Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia,Pharmacological Research, vol. 156, 104797, 2020. 

  3. Sibo Yang, Chao Zhang*, Yuan Bao, Jie Yang and Wei Wu, Binary Output Layer of Extreme Learning Machine for Solving Multi-class Classification Problems, Neural Processing Letters, vol. 52, pp. 153–167, 2020. 

  4. Xianjie Gao, Chao Zhang and Hongwei Zhang, A Refined Non-Asymptotic Tail Bound of Sub-Gaussian Matrix, Journal of Mathematical Research with Applications, vol. 40, no. 5, pp. 543-550, 2020. 

[2019]

  1. Xianjie Gao, Maolin Shi, Xueguan Song, Chao Zhang* and Hongwei Zhang. Recurrent neural networks for real-time prediction of TBM operating parameters, Automation in Construction, vol. 98, pp. 225-235, 2019.  Feb. 

  2. Junhong Zhao, Maolin Shi, Gang Hu, Xueguan Song, Chao Zhang*, Dacheng Tao and Wei Wu, A Data-Driven Framework for Tunnel Geological-Type Prediction Based on TBM Operating Data, IEEE Access, vol. 7, pp. 66703-66713, 2019.

  3. Xianjie Gao, Chao Zhang* and Hongwei Zhang. Small-Deviation Inequalities for Sums of Random Matrices, Symmetry, vol. 11, pp. 638, 2019.

  4. Sibo Yang, Chao Zhang, Wei Wu. Binary Output Layer of Feedforward Neural Networks for Solving Multi-Class Classification Problems, IEEE Access, vol. 7, pp. 5085-5094, 2019.

  5. Chun Hua, Feng Li, Chao Zhang, Jie Yang and Wei Wu. A Genetic XK-Means Algorithm with Empty Cluster Reassignment, Symmetry, vol.11, pp. 744, 2019. 

  6. Xue Xu; Jianqiang Li; Jinfeng Zou; Xiaowen Feng; Chao Zhang; Ruiqing Zheng; Weixiang Duanmu; Arnab Saha-Mandal; Zhong Ming; Edwin Wang, Association of Germline Variants in Natural Killer Cells With Tumor Immune Microenvironment Subtypes, Tumor-Infiltrating Lymphocytes, Immunotherapy Response, Clinical Outcomes, and Cancer Risk. JAMA Network Open, vol. 2, no. 9, 2019;

  7. Hua Chun, Chao Zhang, Yan Liu, Wei Wu, Mongolian Similar Elements Clustering via Immune Clone Algorithm, Journal of Mathematical Research with Applications, vol. 39, no.6, pp. 745-754, 2019.

[2018]

  1. Wei Sun, Maolin Shi, Chao Zhang, Junhong Zhao and Xueguan Song. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data, Automation in Construction, vol. 92, pp. 23-34, 2018.

  2. Yan Liu, Dakun Yang and Chao Zhang. Relaxed conditions for convergence analysis of online back-propagation algorithm with L_2 regularizer for Sigma-Pi-Sigma neural network, Neurocomputing, vol. 272, pp. 163-169, 2018.

[2017]

  1. Chao Zhang, Lei Du and Dacheng Tao. LSV-Based Tail Inequalities for Sums of Random Matrices, Neural Computation, vol. 29, no. 1, pp. 247-262, 2017.

[2016]

  1. Xue Xu, Chao Zhang, Pidong Li, Feilong Zhang, Kuo Gao, Jianxin Chen and Hongcai Shang. Drug-symptom networking: Linking drug-likeness screening to drug discovery, Pharmacological Research, vol. 103, pp. 105-113, 2016.

  2. Mingchen Yao, Chao Zhang, Wei Wu, Online Sequential Double Parallel Extreme Learning Machine for Classifications, Journal of Mathematical Research with Applications, vol. 36, no. 5, pp. 621-630, 2016.

[2015]

  1. Mingchen Yao, Chao Zhang and Wei Wu. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples, Discrete Dynamics in Nature and Society, vol. 2015, 2015.

[2013]

  1. Chao Zhang and Dacheng Tao. Risk Bounds of Learning Processes for Lévy Processes, Journal of Machine Learning Research, vol. 14, pp. 351-376, 2013. 

  2. Chao Zhang and Dacheng Tao. Structure of Indicator Function Classes with Finite Vapnik-Chervonenkis Dimensions, IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 7, pp. 1156-1160, 2013. 

  3. Chao Zhang. Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence. UAI-2013. 

[2012]

  1. Chao Zhang, Wei Bian, Dacheng Tao and Weisi Lin. Discretized-Vapnik-Chervonenkis Dimension for Analyzing Complexity of Real Function Classes. IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, pp. 1461-1472, 2012. 

  2. Chao Zhang and Dacheng Tao. Generalization Bounds of ERM-Based Learning Processes for Continuous-Time Markov Chains. IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 12, pp. 1872-1883, 2012.

  3. Chao Zhang, Lei Zhang and Jieping Ye. Generalization Bounds for Domain Adaptation. NIPS-2012. 

[2011]

  1. Chao Zhang, Jie Yang and Wei Wu. Binary Higher-Order Neural Networks for Realizing Boolean Functions. IEEE Transactions on Neural Networks, vol. 22, no. 5, pp. 701-713, 2011. 

  2. Chao Zhang and Dacheng Tao. Generalization Bound for Infinitely Divisible Empirical Process. AISTATS-2011. 

  3. Chao Zhang and Dacheng Tao. Risk Bounds for Infinitely Divisible Distribution. UAI-2011. 

[2010]

  1. Chao Zhang and Dacheng Tao. Risk Bounds for Lévy Processes in the PAC-Learning Framework. AISTATS-2010. 

  2. Chao Zhang and Dacheng Tao. Error bounds for real function classes based on discretized Vapnik-Chervonenkis dimensions. Australian J. Intell. Inform. Process. Syst. Mach. Learn. Appl. I, vol. 12, no. 3, 2010. (ICONIP 2010)

  3. 张超, 李正学, 陈先华, 熊焱. 用在线梯度法训练积单元神经网络的收敛性分析, 高等学校计算数学学报, vol. 32, no. 3, pp. 261-274, 2010.

  4. 熊焱, 吴微, 张超, 亢喜岱. 基于灰色关联分析的高阶神经网络剪枝算法, 大连理工大学学报, vol. 50, no. 3, pp: 463-468, 2010.

[2009]

  1. Huisheng Zhang, Chao Zhang and Wei Wu. Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks. Discrete Dynamics in Nature and Society, 2009.

[2008]

  1. Chao Zhang, Wei Wu, Xianhua Chen and Yan Xiong. Convergence of BP Algorithm for Product Unit Neural Networks with Exponential Weights. Neurocomputing, vol. 72, no. 1-3, pp. 513-520, 2008. 

  2. 熊焱, 张超. Pi-sigma神经网络的带动量项的异步批处理梯度算法收敛性, 应用数学, vol. 21, no. 1, pp: 207-212, 2008.

[2007] 

  1. Chao Zhang, Wei Wu and Yan Xiong. Convergence Analysis of Batch Gradient Algorithm for Three Classes of Sigma-Pi Neural Networks. Neural Processing Letters, vol. 26, no. 3, pp. 177-189, 2007. 

  2. Wei Wu, Yan Xiong, Xidai Kang and Chao Zhang. Training Pi-Sigma Neural Network by Online Gradient Algorithm with Penalty for Small Weights. Neural Computation, vol. 19, pp. 3356-3368, 2007. 

[2006]

  1. Huifang Lu, Wei Wu, Chao Zhang and Yan Xiong, Convergence of Gradient Descent Algorithm for Pi-Sigma Neural Networks, Journal of Information and Computational Science, vol. 3, no. 3, pp. 503-509, 2006. 

教育经历

[1]   2004.9-2008.12

大连理工大学  |  计算数学  |  博士

[2]   2000.9-2004.7

大连理工大学  |  数学与应用数学  |  学士

[3]   1997.9-2000.7

辽师大附中  |  计算数学[高校教师]  |  博士

工作经历

[1]   2009.2-2011.10

南洋理工大学  |  研究员  |  Research Fellow

[2]   2013.10-至今

大连理工大学  |  School of Mathematical Sciences  |  副教授  |  Associate Professor

[3]   2012.4-2013.10

亚利桑那州立大学  |  博士后  |  Postdoctor

研究方向

  • [1]   机器学习
  • [2]   神经网络/深度学习
  • [3]   数据挖掘
  • [4]   随机矩阵
  • [5]   学习理论
  • 团队成员

    机器学习及研究组

    有副教授两名,讲...