講座主題：基于復(fù)雜網(wǎng)絡(luò)和深度學(xué)習(xí)的多源信息融合與應(yīng)用
主講人：高忠科
工作單位：天津大學(xué)
講座時間：2019年10月30日（周三）上午10:30
講座地點：數(shù)學(xué)院大會議室341
主辦單位：煙臺大學(xué)數(shù)學(xué)與信息科學(xué)學(xué)院
內(nèi)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.
主講人介紹：
高忠科，天津大學(xué)電氣自動化與信息工程學(xué)院教授、博士生導(dǎo)師，國家優(yōu)秀青年科學(xué)基金獲得者。主要研究方向為復(fù)雜網(wǎng)絡(luò)多源信息融合理論、新型傳感器技術(shù)、多相流檢測、腦機融合與混合智能等，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等國際期刊上發(fā)表SCI檢索論文100余篇，論文SCI他引2000余次，12篇第一作者論文入選ESI高被引論文；在德國Springer出版社出版英文學(xué)術(shù)專著一部；第一發(fā)明人中國發(fā)明專利32項。主持國家級項目6項，包括4項國家自然科學(xué)基金項目。獲2013年全國百篇優(yōu)秀博士學(xué)位論文提名獎，入選天津市131創(chuàng)新型人才培養(yǎng)工程和天津市創(chuàng)新人才推進計劃青年科技優(yōu)秀人才，2018年和2019年2次獲得英國皇家物理學(xué)會(IOP)高被引中國作者獎。
