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澳大利亚麦考瑞大学吴佳博士学术报告会通知

阅读量:302 发布时间:2017-12-11 11:02:34

报告时间:12.12上午10:15

报告地点:一教南楼109党员会议室

报告人:澳大利亚麦考瑞大学 吴佳博士

主题:复杂大数据挖掘的趋势: 从基础到真实世界的人工智能

联系人:樊谨

   Jia Wu(吴佳):澳大利亚麦考瑞大学(Top157,上海交大排名2017)助理教授,博士、IEEE会员。主要研究领域为数据挖掘、机器学习、人工智能,及其在商业、工业、生物信息学、医疗信息学等领域的应用。迄今,在国际学术期刊和会议上共发表论文60多篇, 包括IEEE Transactions on Knowledge and Data EngineeringIEEE Transactions on Neural Network and Learning SystemsIEEE Transactions on CyberneticsACM Transactions on Knowledge Discovery DataPattern RecognitionIJCAIAAAIICDMSDMCIKMDASFAA等。指导学生获得2017顶级人工神经网络会议IEEEInternational Joint Conference on Neural Networks的最佳学生论文奖。2014顶级国际数据挖掘会议IEEE International Conference on Data Mining的最佳论文提名奖。

    现任现任数据挖掘著名期刊《ACM Transactions on Knowledge Discovery from Data》(CCF B类)和《Journal of Network and Computer Applications》(中科院一区)副主编和Complexity Journal (中科院一区)客座主编。担任国际顶级神经网络大会201620172018 International Joint Conference on Neural Networks的专题分会主席 (Special Session Chair)。担任顶级人工智能国际会议International Joint Conference on Artificial Intelligence IJCAI20172018、顶级数据挖掘国际会议SIAM International Conference on Data Mining SDM2018和著名亚太数据挖掘会议Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD2018的高级程序委员 (Senior Program Committee),顶级国际学术会议的程序委员 (Program Committee), 包括IJCAIAAAIKDDICDMSDMCIKMDASFAAPAKDDIJCNN等。并应邀为多家顶级国际学术期刊和会议担任评审委员会委员,国际学期刊包括IEEE TKDEIEEE TSMCIEEETPAMIIEEETNNLSACM TKDDPR等,国际学术会议包括IJCAIAAAIICDMSDMPAKDDIJCNNDASFAACIKM等。

   

Advances in Mining ComplexBig Data: From Foundations to Real-World Artificial Intelligence

Big Data is an emerging paradigm, characterized by complex information that is beyond the processing capability of conventional tools. Traditional data analytics methods are commonly used in many applications, such as text classification and image recognition, and these data are often required to be represented as vectors for analysis purposes. While there are many real-world data objects that contain rich structure information, such as chemical compounds in bio-pharmacy, brain regions in brain networks and users in social networks. The simple feature-vector representation inherently loses the structure information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized. Data may also come from heterogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, or semi-structured data. Processing, mining, and learning complex data refers to an advanced study area of data mining and knowledge discoverythat concerns the development and analysis of approaches for discovering patterns and learning models for data with complex structures (e.g.,time series, sequences, graphs, and bag constrained data). These kinds of data are commonly encountered in many artificial intelligence applications, such as brain science. Complex data poses new challenges for current research in data mining and knowledge discovery as new processing, mining, and learning methods are required.

  


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