马尽文的个人简介
马尽文，北京大学数学科学学院教授、博士生导师。
学习与工作经历
1992年南开大学概率论与数理统计专业博士毕业。
1992年到汕头大学数学系、数学研究所工作。
1999年应用数学专业教授。
2001年9月调入北京大学数学科学学院信息科学系工作，现为应用数学专业教授、博士生导师、系主任。
从1995年至2004年，多次到香港中文大学计算机科学与工程学系进行合作研究，担任副研究员(ResearchAssociate)或研究员(ResearchFellow)。
2005年9月至2006年8月在日本理化学研究所(RIKEN)脑科学研究所Amari研究组进行科学研究，担任研究科学家(ResearchScientist)。
2011年9月至2012年3月在美国休斯顿卫理会医院系统科研中心系统医学和生物工程系进行科学研究，担任科学家(Scientist)。
研究方向
神经计算，独立分量分析(ICA)，统计学习理论与算法，智能信息处理和生物信息学。
科研成就
从上世纪九十年代初开始从事人工神经网络和学习算法方面的理论及其应用研究，主要针对大数据的挑战进行数据挖掘、机器学习和智能信息处理等方面的研究。已发表学术论文100余篇，其中被SCI收录50余篇，被引用1000余次，多篇论文发表在《NeuralComputation》、《IEEETrans.onSMC-B》、《IEEETrans.onImageProcessing》、《NeuralNetworks》、《PatternRecognition》等国际著名期刊和SIGIR、SIGKDD等国际重要学术会议文集上。在高斯混合模型的参数学习和自适应模型选择方面建立了一套系统的理论与有效的学习算法,并被广泛地应用于聚类分析、模型识别和图像处理的等领域。先后主持国家自然科学基金项目6项、国家科技重大专项子项1项和省部级及横行科研基金项目8项。
曾被邀请到美国、英国、加拿大、澳大利亚、日本、台湾和香港等国家和地区参加国际学术会议和进行学术交流20余次，交流学术论文30余篇。6次被国内外学术会议邀请做特邀报告。
荣誉头衔
现担任中国工业与应用数学学会理事，中国电子学会信号处理分会委员，《TheScientificWorldJournal》、《JournalofIndustrialMathematics》、《信号处理》等杂志的编委、《数学计算》杂志的主编。曾多次担任ISNN,ICIC,ICONIP,ICSP等重要国际学术会议的程序委员会议委员。并担任1999年中国神经网络和信号处理学术会议的程序委员会主席。
主要论文
1. 自适应模型选择与聚类分析(Adaptive Model Selection and Clustering Analysis)
[1.1] Hongyan Wang and Jinwen Ma, Simultaneous model selection and feature selection via BYY harmony learning, Lecture Notes in Computer Science, vol.6676, pp: 47-56, 2011.
[1.2] Yanqiao Zhu and Jinwen Ma, A stage by stage pruning algorithms for detecting the number of clusters in a dataset, Lecture Notes in Computer Science, vol. 6215, pp: 222-229, 2010.
[1.3] Jinwen Ma, Jianfeng Liu and Zhijie Ren, Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning, Pattern Recognition, vol.42, pp:2659-2670, 2009.
[1.4] Chonglun Fang and Jinwen Ma, A novel ku2019-means algorithm for clustering analysis, Proceedings of the 2 International Conference on Biomedical Engineering and Informatics (BMEI, 2009), 17-19 October 2009, Tianjin,China.
[1.5] Lin Wang and Jinwen Ma, A kurtosis and skewness based criterion for model selection on Gaussian mixture, Proceedings of the 2 International Conference on Biomedical Engineering and Informatics (BMEI, 2009), 17-19 October 2009, Tianjin, China.
[1.6] Jinwen Ma and Xuefen He, A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection, Pattern Recognition Letters, vol.29, pp: 701-711, 2008.
[1.7] Lei Li and Jinwen Ma, A BYY scale-incremental EM algorithm for Gaussian mixture learning, Applied Mathematics and Computation, vol.205, pp: 832-840, 2008.
[1.8] Hengyu Wang, Lei Li and Jinwen Ma, The competitive EM algorithm for Gaussian mixtures with BYY harmony criterion, Lecture Notes in Computer Science, vol.5226, pp: 552-560, 2008.
[1.10] Lei Li and Jinwen Ma, A BYY split-and-merge EM algorithm for Gaussian mixture learning, Lecture Notes in Computer Science, vol.5263, pp: 600-609, 2008.
[1.11] Zhijie Ren, Jinwen Ma, BYY Harmony Learning on Weibull Mixture with Automated Model Selection, Lecture Notes in Computer Science, vol.5263, pp: 589-599, 2008.
[1.12] Hongyan Wang and Jinwen Ma, BYY harmony enforcing regularization for Gaussian mixture learning, Proceedings of the 9 International Conference on Signal Processing (ICSP, 2008, 26-29 Oct., Beijing,China), pp: 1664-1667.
[1.13] Jinwen Ma and Jianfeng Liu, The BYY annealing learning algorithm for Gaussian mixture with automated model selection, Pattern Recognition, vol.40, pp:2029-2037, 2007.
[1.14] Kai Huang, Le Wang, and Jinwen Ma, Efficient training of RBF networks via the BYY automated model selection learning algorithms, , Lecture Notes in Computer Science， vol.4491, pp: 1183-1192，2007.
[1.15] Jinwen Ma, Automated model selection (AMS) on finite mixtures: a theoretical analysis, Proceedings of 2006 International Joint Conference on Neural Networks (IJCNNu201906), pp: 8255-8261, 2006.
[1.16] Jinwen Ma and Le Wang, BYY harmony learning on finite mixture: adaptive gradient implementation and a floating RPCL mechanism, Neural Processing Letters, vol.24, no.1, pp: 19-40, 2006.
[1.17] Jinwen Ma and Taijun Wang, A cost-function approach to rival penalized Competitive learning (RPCL), IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol.36, no.4, pp: 722-737, 2006.
[1.18] Jinwen Ma and Bin Cao, The Mahalanobis distance based rival penalized competitive learning algorithm, Lecture Notes in Computer Science，vol.3971, pp: 442-447, 2006.
[1.19] Jinwen Ma and Qicai He, A dynamic merge-or-split learning algorithm on Gaussian mixture for automated model selection, Lecture Notes in Computer Science， vol.3578, pp: 203-210, 2005.
[1.20] Jinwen Ma, Bin Gao, Yang Wang, and Qiansheng Cheng, Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection, International Journal of Pattern Recognition and Artificial Intelligence, vol.19, no.5, pp: 701-713, 2005.
[1.21] Jinwen Ma, Taijun Wang, and Lei Xu, A gradient BYY harmony learning rule on Gaussian mixture with automated model selection, Neurocomputing, vol.56, pp: 481-487, 2004.
[1.22] Jinwen Ma and Taijun Wang, Entropy penalized automated model selection on Gaussian mixture, International Journal of Pattern Recognition and Artificial Intelligence, vol.18, no.8, pp: 1501-1512, 2004.
[1.23] Hua-Jun Zeng, Qi-Cai He, Zheng Chen, Wei-Ying Ma, and Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th International ACM Conference on Research and Development in Information Retrieval (SIGIRu201904), Sheffield, UK, July 25-29, 2004,pp: 210-217.
2. 图像分析与纹理分类(Image Analysis and Texture Classification)
[2.1] Chonglun Fang and Jinwen Ma, A fixed-point EM algorithm for straight line detection, Lecture Notes in Computer Science, vol.6676,pp:136-143,2011.
[2.2] Yongsheng Dong and Jinwen Ma, Contourlet-based texture classification with product Bernoulli distributions, Lecture Notes in Computer Science, vol.6676,pp:9-18,2011.
[2.3] Yongsheng Dong and Jinwen Ma, Wavelet-based image texture classification using local energy histograms, IEEE Signal Processing Letters, vol.18.no.4, pp: 247-250, 2011.
[2.4] Jinwen Ma and Lei Li, Automatic straight line detection through fixed-point BYY harmony learning, Lecture Notes in Computer Science, vol.5226, pp: 569-576, 2008.
[2.5] Gang Chen, Lei Li, Jinwen Ma, A gradient BYY harmony learning algorithm for straight line detection, Lecture Notes in Computer Science, vol.5263, pp: 618-626, 2008.
[2.6] Zhiwu Lu, Qiansheng Cheng, Jinwen Ma, A gradient BYY harmony learning algorithm on mixture of experts for curve detection, Lecture Notes in Computer Science， vol.3578, pp: 250-257，2005.
3. 独立分量分析(Independent Component Analysis）
[3.1] Fei Ge and Jinwen Ma, An efficient pairwise kurtosis optimization algorithm for independent component analysis, Communications in Computer and Information Science, vol.93, pp:94-101, 2010.
[3.2] Fei Ge and Jinwen Ma, Spurious solution of the maximum likelihood approach to ICA, IEEE Signal Processing Letters, vol.17.no.7, pp: 655-658, 2010.
[3.3] Fei Ge and Jinwen Ma, Analysis of the Kurtosis-sum objective function for ICA, Lecture Notes in Computer Science, vol.5263, pp: 579-588,2008.
[3.4] Zhe Chen and Jinwen Ma, Contrast functions for non-circular and circular sources separation in complex-valued ICA, Proceedings of 2006 IEEE International Joint Conference on Neural Networks (IJCNNu201906), pp: 1192-1199, 2006.
[3.5] Jinwen Ma , Zhe Chen and Shun-ichi Amari, Analysis of feasible solutions of the ICA problem under the one-bit-matching condition, Lecture Notes in Computer Science，vol.3889, pp: 838-845, 2006.
[3.6] Jinwen Ma, Dengpan Gao, Fei Ge and Shun-ichi Amari, A one-bit-matching learning algorithm for independent component analysis, Lecture Notes in Computer Science，vol.3889, pp: 173-180, 2006.
[3.7] Jinwen Ma, Fei Ge and Dengpan Gao, Two adaptive matching learning algorithms for independent component analysis, Lecture Notes in Artificial Intelligence, vol.3801, pp: 915-920, 2005.
[3.8] Dengpan Gao, Jinwen Ma and Qiansheng Cheng, An alternative switching criterion for independent component analysis (ICA), Neurocomputing， vol.68, pp: 267-272, 2005.
[3.9] Jinwen Ma, Zhiyong Liu and Lei Xu, A further result on the ICA one-bit-matching conjecture, Neural Computation, vol.17, no.2, pp: 331-334, 2005.
4. 生物信息学 (Bioinformatics)
[4.1] Wei Wang and Jinwen Ma, Density Based merging search of functional modules in protein-protein interaction (PPI) networks, Lecture Notes in Computer Science, vol. 6215, pp: 634-649, 2010.
[4.2] Fuhai Li, X Zhou, J Ma, and STC Wong, Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis, IEEE Transactions on Medical Imaging, vol.29, no.1, pp: 95-105, 2010.
[4.3] Wei Xiong, Zhibin Cai, and Jinwen Ma, A DSRPCL-SVM approach to informative gene analysis, Genomics, Proteomics & Bioinformatics, vol.6, no.2, pp: 83-90, 2008.
[4.4] Fuhai Li, Xiaobo Zhou, Jinmin Zhu, Wieming Xia, Jinwen Ma and Stephen T. C. Wong, Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals, Neuroinformatics, vol. 6, no.2, pp: 97-108, 2008.
[4.5] Fuhai Li，XiaoboZhou， Jinmin Zhu，Jinwen Ma，XudongHuangm and Stephen TC Wong, High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles, BMC Biotechnology , 2007, 7: 66.
[4.6] F. Li, X. Zhou, J. Ma, & Stephen T. C. Wong, An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening, Journal of Microscopy, Vol.226, pt 2, pp: 121-132, 2007.
[4.7] Liangliang Wang and Jinwen Ma, Informative gene set selection via distance sensitive rival penalized competitive learning and redundancy analysis, Lecture Notes in Computer Science，vol.4491, pp: 1227-1236, 2007.
[4.8] Liangliang Wang and Jinwen Ma, A post-filtering gene selection algorithm based on redundancy and multi-gene analysis, International Journal of Information Technology, vol.11, no.8, pp: 36-44, 2005.
[4.9] Jinwen Ma, Minghua Deng, Application of DNA microarray data to medicine, Physics (in Chinese), vol.34, no.5, pp: 371-380, 2005.
[4.10] Jinwen Ma, Fuhai Li, and Jianfeng Liu, Non-parametric statistical tests for informative gene selection, Lecture Notes in Computer Science，vol.3498, pp: 697-702, 2005.
[4.11] Jun Luo and Jinwen Ma, A multi-population X-2 test approach to informative gene selection, Lecture Notes in Computer Science，vol. 3578, pp: 406-413, 2005.
[4.12] Fei Ge and Jinwen Ma, An information criterion for informative gene selection, Lecture Notes in Computer Science，vol.3498, pp: 703-708, 2005.
[4.13] Lin Deng, Jinwen Ma, and Jian Pei, Rank sum method for related gene selection and its application to tumor diagnosis, Chinese Science Bulletin, vol.49, no.15, pp: 1652-1657, 2004.
[4.14] Lin Deng, Jian Pei, Jinwen Ma, and Dik Lun Lee, A rank sum test method for informative gene discovery, Proceedings of the Tenth ACM International Conference on Knowledge Discovery and Data Mining (SIGKDDu201904), Seattle, Washington, USA, August 22-25, 2004, pp: 410-419.
5. EM算法 (EM Algorithm)
[5.1] Yan Yang and Jinwen Ma, Asymptotic convergence properties of the EM algorithm for mixture of experts, Neural Computation, In Press, 2011.
[5.2] Yan Yang and Jinwen Ma, An efficient EM approach to parameter learning of the mixture of Gaussian processes, Lecture Notes in Computer Science，vol. 6676, pp: 165-174, 2009.
[5.3] Yan Yang and Jinwen Ma, A single loop EM algorithm for the mixture of experts architecture, Lecture Notes in Computer Science，vol. 5552, pp: 959-968, 2009.
[5.4] Jinwen Ma and Shunqun Fu, On the correct convergence of the EM algorithm for Gaussian mixtures, Pattern Recognition， vol.38, no.12, pp: 2602-2611, 2005.
[5.5] Jinwen Ma and Lei Xu, Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture, Neurocomputing，vol.68, pp: 105-129, 2005.
[5.6] Jinwen Ma, Lei Xu, and Michael I. Jordan, Asymptotic convergence rate of the EM algorithm for Gaussian mixtures, Neural Computation，vol.12, no.12, pp: 2881-2907, 2000.
6. 联想记忆与时空序列(Associative Memory and Spatio-temporal Sequence)
[6.1] Fuhai Li, Jinwen Ma, and Dezhi Huang, MFCC and SVM based recognition of Chinese vowels, Lecture Notes in Artificial Intelligence, vol.3802, pp: 812-819, 2005.
[6.2] Jinwen Ma, The capacity of time-delay recurrent neural network for storing spatio-temporal sequences, Neurocomputing, vol.62, pp: 19-27, 2004.
[6.3] Jianwei Wu, Jinwen Ma, and Qiansheng Cheng, Further results on the asymptotic memory capacity of the generalized Hopfield network, Neural Processing Letters, vol.20, pp: 23-38, 2004.
[6.4] Jinwen Ma, A hybrid neural network of addressable and content-addressable memory, International Journal of Neural Systems, vol.13, no.3, pp: 205-213, 2003.
[6.5] Jinwen Ma and Dezhi Huang, A neural network filter for complex spatio-temporal patterns, Proceedings of the International Joint Conference on Neural Networks (IJCNNu201902), Hawaii, USA, May 12-17 2002, vol.1, pp: 1028-1033.
[6.6] Jinwen Ma, A neural network approach to real-time pattern recognition, International Journal of Pattern Recognition and Artificial Intelligence, vol.15, no.6, pp: 937-947, 2001.
[6.7] Jinwen Ma, The asymptotic memory capacity of the generalized Hopfield networks，Neural Networks, vol.12, no.9, pp: 1207-1212, 1999.
[6.8] Jinwen Ma, The object perceptron learning algorithm on generalised Hopfield networks for associative memory, Neural Computing & Applications, vol.8, no.1, pp: 25-32, 1999.
[6.9] Jinwen Ma, The stability of the generalized Hopfield networks in randomly asynchronous mode, Neural Networks, vol.10, no.6, pp: 1109-1116, 1997.
[6.10] Jinwen Ma, Simplex memory neural networks, Neural Networks, vol.10, no.1, pp: 25-29, 1997.
