导师代码： 11708
导师姓名： 邵俊明
性别： 男
特称：

职称： 教授
学位： 理学博士学位
属性： 专职
电子邮件： junmshao@uestc.edu.cn

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学术经历： 2008.09 - 2011.11 慕尼黑大学 （博士）
2011.11 - 2012.07 慕尼黑工业大学 （博士后）
2012.08 - 2013.12 美因茨大学 （洪堡****）

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个人简介： 一直致力于数据挖掘，机器学习的理论研究及其在交叉学科领域的实践研究，在国际学术期刊和会议上发表高水平学术论文30余篇。博士期间(2008 - 2011)，在欧洲著名的慕尼黑大学数据挖掘小组从事数据挖据理论研究。受自然同步现象的启发，首次创新性的将其原理引入数据挖掘领域。其相关论文分别发表在数据挖掘的三大会议（SIGKDD，ICDM，SDM）及期刊 IEEE TKDE上。在数据挖掘理论研究的同时，并致力于将其应用于大脑神经影像及环境科学等交叉学科领域，取得一批研究成果。论文分别发表在相关领域的权威期刊上，如在神经科学期刊Neurobiology of Aging，水资源研究领域顶级期刊Water Research、权威期刊Environmental Modelling & Software等上发表论文10余篇。两篇关于大脑神经纤维的自动聚类 （Fiber Clustering）论文分别被ICDM研讨会议组和美国IGI Global国际出版社评为“最佳论文奖”。于2011年11月提前项目一年左右以“Summa cum laude”最高荣誉毕业，是慕尼黑大学数据挖掘小组成立以来第二个获此殊荣的博士毕业生。博士毕业论文“基于同步原理的数据挖掘”已受德国LAP LAMBERT Academic Publishing学术出版社邀请出版。一直活跃在国际学术前沿，现为包括TKDE在内的4个重要英文期刊评审，在国际学术会议SIGKDD, ICDM, PKDD等上作重要学术报告8次，发表会议论文12篇。目前为大数据研究中心数据挖掘实验室主任。

积极承担或参与了多项交叉学科研究项目，包括洪堡奖学金项目、国家自然科学重点项目、面上项目和青年基金项目、欧盟INTERREG项目以及德国联邦教育及研究部（BMBF）科研项目、四川省科技厅青年项目（****）等，积累了承担和组织实施大型科研项目的经验。

获奖情况

[1] Shao J，“Synchronization-based complex network analysis”，洪堡基金，2012
[2] Shao J，“Synchronization-inspired data mining”(博士论文及答辩)，University of Munich，“Summa Cum Laude”(最高荣誉)，2011
[3] Shao J*, Hahn K, Yang Q, Wohlschlaeger A, Boehm C, Myers N and Plant C, “Combining Time Series Similarity with Density-based Clustering to Identify Fiber Bundles.”, IEEE ICDM BioDM, 最佳论文奖, 2010
[4] Shao J*, Hahn K, Yang Q, Wohlschlaeger A, Boehm C, Myers N and Plant C, “Hierarchical Density-based Clustering of White Matter Tracts in the Human Brain”, IGI Global, 第四届年度杰出研究期刊奖的最佳论文奖,2010.

实验室详细信息请参考： http://dm.uestc.edu.cn/
欢迎对数据挖掘、机器学习、大数据分析、神经科学感兴趣的同学报考！

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科研项目： 主持及参与的主要科研项目

[1] 大数据环境下基于同步原理的数据流挖掘算法研究,国家自然科学基金青年项目，国家自然科学基金委员会，2015-2017， 主持。
[2] 大规模网络挖掘的关键技术及应用研究,四川省科技厅，2016-2019， 主持。
[3] 面向大规模数据流挖掘的关键技术研究， 中央高校基本科研业务费基础研究项目， 2015-2017，主持。
[4] 基于同步的复杂网络，校杰出人才配套科研基金，2013-2016， 主持
[5]大数据结构与关系的度量与简约计算，自然科学基金重点项目，国家自然科学基金委员会,2015-2019，主研。
[6] 基于同步的网络分析，德国洪堡基金 (2012-2013)，8万欧，主持
[7] 可持续蓄洪库的分类与优化, 欧盟INTERREG项目(2008-2012), 主研[8] 基于生物视觉机制的语义图像检索模型及方法 国家自然科学基金(2010-2012)，主研
[8] 基于大脑功能链接的临床fMRI研究, 德国联邦教育及研究部（BMBF）项目， （2008-2013），主研

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研究成果： 代表性论文

[1] Shao, J., Yang, Q., Dang, HV, Schmidt, B., Kramer, S: Scalable Clustering by Iterative Partitioning and Point Attractor Representation, ACM Transaction on Knowledge Discovery from Data (TKDD), 2016. accepted.
[2] Shao, J., Han, Z., Yang, Q., Zhou, T：Community Detection based on Distance Dynamics, Proceedings of the 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2015. (数据挖掘顶级会议)
[3] Shao, J., Ahmadi, Z. and Kramer, S.：Prototype-based Learning on Concept-drifting Data Streams, Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pp. 412-421. 2014. (数据挖掘顶级会议)
[4]Meng, C., Brandl, F., Tahmasian, M., Shao, J., Manoliu, A., Scherr, M., … & Sorg, C.:Aberrant topology of striatum’s connectivity is associated with the number of episodes in depression, Brain 2014: 137; 598–609. （IF>10, JCR 一区）
[5] Shao, J., He, X., Boehm, C., Yang, Q. and Plant, C.:Synchronization-inspired Partitioning and Hierarchical Clustering, IEEE Transactions on Knowledge and Data Engineering, 25(4): 893-905. 2013. (数据挖掘顶级期刊)
[6] Shao, J, Yang, Q, Wohlschlaeger, A, and Sorg, C.:Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining, International Journal of Knowledge Discovery in Bioinformatics, 3(1):14-29, 2013.
[7] Shao, J., He, X., Yang, Q., Plant, C. and Boehm, C.:Robust Synchronization-Based Graph Clustering, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 249-260, 2013.
[8] Tahmasian, M., Knight, D. C., Manoliu, A., Schwerth?ffer, D., Scherr, M., Meng, C., … & Sorg, C.:Aberrant intrinsic connectivity of hippocampus and amygdala overlap in the fronto-insular and dorsomedial-prefrontal cortex in major depressive disorder, Frontiers in human neuroscience, 7, 2013.
[9] Shao, J:Synchronization on Data Mining, LAP LAMBERT Academic Publishing, 2012. （专著）
[10] Shao, J., Myers, N., Yang, Q., Feng, J., Plant, C., B?hm, C., F?rstl, H., Kurz, A., Zimmer, C., Meng, C., Riedl, V., Wohlschl?ger, A. and Sorg, C.:Prediction of Alzheimer’s disease using individual structural connectivity networks, Neurobiology of Aging, 33(12):2756-2765, 2012. （JCR 一区）
[11] Shao J., Yang Q., Wohlschlaeger A. and Sorg C.:Discovering Aberrant Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining, IEEE International Conference on Data Mining (ICDM), Workshop on Biological Data Mining and its Applications in Healthcare (BioDM), pp. 86-93, 2012.
[12] Plant, C, Thai, SM, Shao, J, Theis, F, Meyer-Baese, A, and Boehm, C:Measuring Non-Gaussianity by Phi-transformed and Fuzzy Histograms, Advances in Artificial Neural Systems, 2012.
[13] Yang, Q, Shao, J, and Scholz, M:Prediction of Sustainable Flood Retention Basin Characteristics using a Self-Organizing Map, Environmental Engineering and Management Journal, 2012.
[14] Yang, Q, Shao, J, Scholz, M, Boehm, C, and Plant, C:Multi-label classification model for Sustainable Flood Retention Basins, Environmental Modelling & Software 32 (2012): 27-36.
[15] Plant, C, Thai, SM, Shao, J, Theis, F, Meyer-Baese, A, and Boehm, C:Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area, Computers, Environment and Urban Systems 36(5): 423-433, 2012.
[16] Shao, J., Yang, Q., Boehm, C. and Plant, C.:Detection of Arbitrarily Oriented Synchronized Clusters in High-dimensional Data, IEEE International Conference on Data Mining (ICDM), pp. 607-616, 2011.
[17] Yang, Q, Scholz, M, and Shao, J:Application of Spatial Statistics as a Screening Tool for Sustainable Flood Retention Basin Management, Water and Environment Journal, 2011.
[18] Yang, Q, Shao, J, Scholz, M, and Plant, C:Feature selection methods for characterizing and classifying adaptive Sustainable Flood Retention Basins, Water Research, 45(3):993-1004, 2011. （JCR 一区）
[19] Yang, Q, Shao, J, and Scholz, M:Classification of Water Bodies including Sustainable Flood Retention Basins (SFRB), International Conference on Integrated Water Resources Management, pp. 110-111., 2011.
[20] Mueller, N.S., Haegler, K., Shao, J., Plant, C. and Boehm, C.:Weighted Graph Compression for Parameter-free Clustering WithPaCCo, Proceedings of the 2011 SIAM International Conference on Data Mining (SDM), 932-943, 2011.
[21] Boehm, C., Plant, C., Shao, J.* and Yang, Q.:Clustering by synchronization, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), 583-592, 2010. (数据挖掘顶级会议， *第一作者及通讯作者)
[22] Shao, J., Boehm, C., Yang, Q. and Plant, C.:Synchronization Based Outlier Detection, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010), 245-260, 2010.
[23] Boehm, C., Feng, J., He, X., Mai, S. M., Plant, C. and Shao, J.:A Novel Similarity Measure for Fiber Clustering using Longest Common Subsequence, ACM SIGKDD Workshop on Data Mining for Medicine and Healthcare (DMMH), pp. 1-9, 2011.
[24] Shao, J., Hahn, K., Yang, Q., Wohlschlaeger, A., Boehm, C., Myers, N. and Plant, C.:Hierarchical Density-based Clustering of White Matter Tracts in the Human Brain, International Journal of Knowledge Discovery in Bioinformatics 1(4), 1-26, 2010. （最佳论文奖）
[25] Shao, J., Hahn, K., Yang, Q., Boehm, C., Wohlschlaeger, A., Myers, N. and Plant, C.:Combining Time Series Similarity with Density-Based Clustering to Identify Fiber Bundles in the Human Brain, Proceedings of International Conference on Data Mining (ICDM), Workshop on Biological Data Mining and its Applications in Healthcare, 747-754, 2010. （最佳论文奖）
[26] Shao, J, Wohlschl?ger, A, Hahn, C, Boehm, C, and Plant, C.:Density-based Clustering of White Matter Tracts in the Human Brain with Dynamic Time Warping, European Workshop on Mining Massive Data Sets (EMMDS ), pp. 1101-1108,2009.
[27] Shao, J, He, D, and Yang, Q :Multi-semantic Scene Classification Based on Region of Interest, CIMCA/IAWTIC/ISE, pp.732-737,2008.
[28] He, D, Shao, J, Gen, N, and Yang, Q :A Model for Image Categorization Based on Biological Visual Mechanism, New Zealand Journal of Agricultural Research, 50(5) :781-787,2007.

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专业研究方向： 专业名称 研究方向 招生类别
081200计算机科学与技术 02机器智能与模式识别 博士
081200计算机科学与技术 06云计算与大数据处理 博士
085400电子信息 07不区分研究方向 博士
081200计算机科学与技术 02机器智能与模式识别 硕士
081200计算机科学与技术 06云计算与大数据处理 硕士

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