报告人：高忠科
时间：2022年7月28日（周四）14:00
会议链接：腾讯线上会议码：417-797-559
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 ****ysis of time series and fruitful achievements have been obtained. Complex network and deep learning ****ysis of time series open up new venues to address interdisciplinary challenges in multiphase flow, brain-computer interface, and rehabilitation engineering. Some novel methodologies and their applications in this research area will be introduced.
个人介绍：
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，天津市杰出青年科学基金获得者，全球高被引科学家，中国高被引学者，天津市中青年科技创新领军人才。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、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 Systems, Man, and Cybernetics: Systems等国际期刊上发表SCI检索论文130余篇；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76项。主持国家级省部级项目10余项。获2021年强国青年科学家提名，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，入选“全球顶尖前10万科学家”榜单。