主讲人介绍：

高忠科，天津大学电气自动化与信息工程学院教授、博士生导师，国家优秀青年科学基金获得者，全球高被引科学家。主要研究方向为复杂网络多源信息融合理论、新型传感器技术、多相流检测、脑机融合与混合智能等，已在IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Industrial Informatics、IEEE Transactions on Instrumentation and Measurement、IEEE Transactions on Systems, Man, and Cybernetics: Systems、Knowledge-Based Systems、Chemical Engineering Journal等国际期刊上发表SCI检索论文100余篇，论文SCI他引2000余次，12篇第一作者论文入选ESI高被引论文；在德国Springer出版社出版英文学术专著一部；第一发明人中国发明专利32项。主持国家级项目6项，包括4项国家自然科学基金项目。获2013年全国百篇优秀博士学位论文提名奖，入选天津市131创新型人才培养工程和天津市创新人才推进计划青年科技优秀人才，2018年和2019年2次获得英国皇家物理学会(IOP)高被引中国作者奖。

内容摘要：

Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network and deep learning have been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network and deep learning analysis of time series open up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, economics and traffic systems. Some novel methodologies and their applications in this research area will be introduced.