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邵俊明,EditSprings,艾德思

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编辑 锁定 邵俊明:男,

电子科技大学 教授.受国家留学基金委LMU-CSC(慕尼黑大学-留学基金委)项目资助,于2008年赴德国慕尼黑大学计算机科学系世界著名数据挖掘小组攻读博士学位.在攻读博士期间,主要从事数据挖掘的理论研究极其在脑科学等交叉学科的应用研究,其相关文章发表在数据挖掘的三大顶级会议(ACM SIGKDD,IEEE ICDM,SIAM SDM)及权威期刊 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水文水资源等交叉学科领域,取得一批原创性研究成果

文章分别发表在相关领域的权威期刊上,如神经科学权威期刊Neurobiology of Aging(IF=,一区期刊),Brain (IF=,一区期刊);水资源研究领域顶级期刊Water Research(IF=,二区)/权威期刊Environmental Modelling & Software(IF=,二区)等.两篇研究文章分别被国际数据挖掘ICDM研讨会议组和美国IGI Global国际出版社评为" 最佳文章奖 '.完成博士学位文章"Synchronization-inspired Data Mining',并于2011年11月提前项目一年左右以最高荣誉(Summa Cum Laude, 分)通过博士文章答辩获得自然科学博士学位(Dr. rer. Nat.),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.

中文名

邵俊明

国    籍

中华人民共和国

职    业

教授

毕业院校

慕尼黑大学

主要成就

获得慕尼黑大学自然科学博士学位

毕业后,在慕尼黑工业大学从事关于脑科学挖掘的交叉学科应用研究.2012年8月,获德国著名的洪堡基金,成为洪堡学者,继续在德国美因茨大学继续从事数据挖掘的理论和实践研究.2013年12月,被电子科技大学引进,在计算机科学与工程学院担任特聘教授,2014年破格评为博士生导师.成立了数据挖掘实验室,隶属于互联网科学中心和大数据研究中心.

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 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

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小组研究方向主要从事数据挖掘的基础理论研究和应用研究,主要但不仅限于:

― 基于同步的数据挖据算法研究(聚类/分类/噪声检测)

― 大数据环境下数据流的算法研究(概念漂移分析和处理/数据流聚类和分类问题)

― 多源异构网络挖掘(社团挖掘/网络压缩/动态数据分析)

― 基于数据挖掘的脑科学研究(fMRI/DTI, 结构和功能连接分析,多源学习

主要科研项目:

[1]. 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.

[2]. 邵俊明等,Complex Network Analysis by Synchronization,德国洪堡基金,2012 -2014,项目负责人.

[3]. 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.

[4]. 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.

[5]. 邵俊明等, 基于生物视觉机制的语义图像检索模型及方式,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.

[6] 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.

[7]. 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.

[8]. 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.

[9] 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.

[1]. 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.

[2]. 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.

[3]. Yang, Q., Shao, J., and Scholz, M.:Self–organizing map to estimate sustainable flood retention basin types and variables, Environmental Engineering and Management Journal, 13(1), 129-134, 2014.

[4]. 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.

[5]. 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.

[6]. 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.

[7]. Tahmasian, M., Knight, D. C., Manoliu, A., Schwerthöffer, 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.

[8]. Shao, J:Synchronization on Data Mining, LAP LAMBERT Academic Publishing, 2012.

[9]. 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.

[10].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.

[11].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.

[12].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.

[13].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..

[14].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.

[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, Scholz, M, and Shao, J:Application of Spatial Statistics as a Screening Tool for Sustainable Flood Retention Basin Management, Water and Environment Journal, 2011.

[17].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.

[18].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.

[19].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.

[20].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.

[21].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.

[22].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.

[23].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.

[24].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.

[25].Shao, J, Wohlschläger, 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.

[26].Shao, J, He, D, and Yang, Q :Multi-semantic Scene Classification Based on Region of Interest, CIMCA/IAWTIC/ISE, .

[27].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.

教学工作

Current Course:

Data Mining ( 数据挖掘 )(Spring 2015),Computer Science.

UESTC UoG12002: Probability Theory and Mathematical Statistics ( 概率论与数理统计,全英语教学 ) (Fall 2014). [10:20-11:55am Tue/Thu @ A313]

 

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参考资料 1.   电子科技大学教授   .电子科技大学教授 [引用日期2016-12-03] 2.   1   .1 [引用日期2016-12-08]

 

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