2008/09 2011/11 德国慕尼黑大学 计算机系数据挖掘中心 博士 2005/09 2008/07 西北农林科技大学 计算机工程学院 硕士 2001/09 2005/07 西北农林科技大学 计算机工程学院 学士

2012/12 至 今 电子科技大学计算机科学与工程学院 教授 2011/08 2012/12 德国美因茨大学计算机系 博士后（洪堡学者） 2011/11 2012/07 德国慕尼黑工业大学脑科学研究中心 博士后

主要科研项目： [1]. 邵俊明等 大规模网络挖掘的关键技术及应用研究, 四川省青年科技基金(杰青),2016-2019， 项目负责人。 [2] 邵俊明等, “全空间信息系统与智能设施管理”重点专项，多模态时空对象分析与可视化题, 2016-2021, 国家重点研究计划，主研。 [3]邵俊明等，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，项目负责人。 [4]. 邵俊明等，Complex Network Analysis by Synchronization,德国洪堡基金,2012 -2014，项目负责人。 [5]. 邵俊明，基于同步原理的网络数据挖掘，校科研启动基金,2013-2016，项目负责人。 [6]. 邵俊明等，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。 [7]. 邵俊明等， 基于生物视觉机制的语义图像检索模型及方法,国家自然科学基金面上项目,国家自然科学基金委员会,2010-2012， 主研。 [8] 邵俊明等，可持续蓄洪库的分类与优化, 欧盟INTERREG项目，2008-2012, 主研。 [9]. 邵俊明， Clustering algorithms for the analysis of Diffusion Tensor Images，国家留学基金委，2008-2011， 项目负责人。 [10]. 邵俊明等，Functional connectivity of the resting brain paves the way for clinical fMRI, 德国联邦教育及研究部（BMBF）项目,2008-2013，主研。 [11] 邵俊明等，Intrinsic Functional Brain Networks in Healthy and Diseased Brains,Volkswagen基金、老年性痴呆研究项目和慕尼黑工大项目,2007-2014, 主研。

代表性论文： [1]. Junming Shao, Xinzuo Wang, Qinli Yang, Claudia Plant and Christian Boehm. Synchronization-based Scalable Subspace Clustering of High-dimensional Data, Knowledge and Information Systems (KIAS), 1-29, 2016. [2] Junming Shao , Chen Huang, Qinli Yang, Guangchun Luo. Reliable Semi-supervised Learning, The IEEE International Conference on Data Mining (ICDM), 2016. [3] Junming Shao*, Qinli Yang, Hoang-Vu Dang, Bertil Schmidt, Stefan Kramer. Scalable Clustering by Iterative Partitioning and Point Attractor Representation, ACM Transactions on Knowledge Discovery from Data (TKDD), 2016. [4] Junming Shao*, Zhichao Han, Qinli Yang, Tao Zhou. Community Detection based on Distance Dynamics, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1075-1084. 2015. [5] Maoud Tahmasian*, Junming Shao, Chun Meng, Timo Grimmer, Janine Diehl-Schmid, Behrooz H Yousefi, Stefan Förster, Valentin Riedl, Alexander Drzezga, Christian Sorg. Based on the network degeneration hypothesis: separating individual patients with different neurodegenerative syndromes in a preliminary hybrid PET/MR study, Journal of Nuclear Medicine, jnumed. 115.165464. 2015. [6] 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. [7]. 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. [8]. 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. [9]. 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. [10]. 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. [11]. Shao, J:Synchronization on Data Mining, LAP LAMBERT Academic Publishing, 2012. [12]. Shao, J., Myers, N., Yang, Q., Feng, J., Plant, C., Böhm, C., Förstl, H., Kurz, A., Zimmer, C., Meng, C., Riedl, V., Wohlschläger, A. and Sorg, C.:Prediction of Alzheimer’s disease using individual structural connectivity networks, Neurobiology of Aging, 33(12):2756-2765, 2012. [13].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. [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].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. [16].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. [17].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. [18].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. [19].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. [20].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)