講座主題：基于復(fù)雜網(wǎng)絡(luò)和深度學(xué)習(xí)的多源信息融合及其在腦控康復(fù)中的應(yīng)用
專(zhuān)家姓名：高忠科
工作單位：天津大學(xué)
講座時(shí)間：2021年11月5日 19:00-20:00
講座地點(diǎn)：騰訊會(huì)議ID：582 417 733
主辦單位：煙臺(tái)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 multiphase flow, brain-computer interface,andrehabilitation engineering.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(dòng)化與信息工程學(xué)院教授、博士生導(dǎo)師，國(guó)家優(yōu)秀青年科學(xué)基金獲得者，天津市杰出青年科學(xué)基金獲得者，全球高被引科學(xué)家，天津市中青年科技創(chuàng)新領(lǐng)軍人才。主要研究方向?yàn)閺?fù)雜網(wǎng)絡(luò)多源信息融合理論、新型傳感器技術(shù)、腦機(jī)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 Systems, Man, and Cybernetics: Systems等國(guó)際期刊上發(fā)表SCI檢索論文130余篇；在德國(guó)Springer出版社出版英文學(xué)術(shù)專(zhuān)著一部；第一發(fā)明人中國(guó)發(fā)明專(zhuān)利76項(xiàng)。主持國(guó)家級(jí)省部級(jí)項(xiàng)目10余項(xiàng)。獲2021年強(qiáng)國(guó)青年科學(xué)家提名，2018年和2019年2次獲得英國(guó)皇家物理學(xué)會(huì)(IOP)高被引中國(guó)作者獎(jiǎng)，入選“全球頂尖前10萬(wàn)科學(xué)家”榜單。
