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校庆70周年系列学术报告之七:网络表示学习、深度学习以及海洋新能源应用

永利官网将于2019年5月15日周三举行主题为网络表示学习、深度学习以及海洋新能源应用的学术报告,敬请光临!

   报告题目:网络表示学习、深度学习以及海洋新能源应用
   报告人: 唐宇飞 博士, 美国佛罗里达大西洋大学助理教授
   报告时间:2019年5月15日 周三 上午  10:00
   报告地点:逸夫楼2楼201会议室


报告摘要:
    Networks are ubiquitous and are a part of our common vocabulary. Network science and engineering has emerged as a formal field over the last twenty years  and has seen explosive growth. Ideas from network science are central to companies such as Google, Twitter, Facebook, and LinkedIn. The concepts have also been used to address fundamental problems in diverse fields, such as biology, economics, social sciences, psychology, power systems, telecommunications, public health and marketing. Recent years have seen a surge in approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. These network representation learning (NRL) approaches remove the need for painstaking feature engineering and have led to state-of-the-art results in network-based tasks, such as node classification, node clustering, and link prediction. In this talk, we will cover key advancements in NRL with an emphasis on fundamental advancements made in the last several years. We will introduce our related work, including multi-label network representation learning and topical network embedding, as well as very recent advancements in graph neural networks. Finally, we will introduce our recent work on deep learning applications in marine renewable energy generation system prognostic health management.

报告人简介:
   
唐宇飞博士目前是佛罗里达大西洋大学电气与计算机学院助理教授,领导智能与韧性系统研究课题组。唐博士于2016年从美国罗德岛大学获得电气工程博士学位,师从著名计算智能专家何海波教授。唐博士课题组目前主要研究兴趣有机器学习,网络大数据挖掘,以及在空间物理系统应用。唐博士目前与美国国家新能源实验室(NREL),美国国家东南海洋新能源研究中心(SNMREC)等国际顶尖机构开展紧密合作。唐博士是多种顶级杂志以及会议审稿人,包括IEEE TNNLS,IEEE TSG,IEEE TBD,ICDM,  AAAI等。