报告题目：Advances in Mining Complex Big Data: From Foundations to Real-World Artificial Intelligence以及“武大-麦考瑞大学计算机学科双博士学位项目”介绍
报告日期及时间：2017年10月17日10:30
报告地点：B403
报告人： Jia Wu（吴佳）
报告人单位：澳大利亚麦考瑞大学
报告人简介： Jia Wu（吴佳）：澳大利亚麦考瑞大学计算机学院讲师，博士、Research Associate、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 Engineering、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Cybernetics、ACM Transactions on Knowledge Discovery Data、Pattern Recognition、IJCAI、AAAI、ICDM、SDM、CIKM等。曾获得2014顶级国际数据挖掘会议International Conference on Data Mining的最佳论文提名奖。现任SCI、JCR一区期刊Journal of Network and Computer Applications副主编和Complexity Journal (SCI: 3.514)客座主编。担任国际顶级神经网络大会2016、2017 International Joint Conference on Neural Networks的专题分会主席 (Special Session Chair)、顶级人工智能国际会议International Joint Conference on Artificial Intelligence, IJCAI 2017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IJCAI、AAAI、ICDM、SDM、CIKM等。
报告摘要： 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 discovery that 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.
邀请人： 杜博 教授
