报告题目: Graphs meet Deep Learning: Challenges and Opportunities

主讲人： Jiliang Tang（汤继良）

讲座时间：2019年5月21日（星期二）10:00-11:30
讲座地点：公司中心校区国际学术大讲堂（图书馆西侧）

报告摘要：

Graphs provide a universal representation of data with numerous types while deep learning has demonstrated immense ability in representation learning. Thus, bridging deep learning with graphs presents astounding opportunities to enable general solutions for a variety of real-world problems. However, traditional deep learning techniques that were disruptive for regular grid data such as images and sequences are not immediately applicable to graph-structured data. Therefore, marrying these two areas faces tremendous challenges. In this talk, I will first discuss these opportunities and challenges, then share a series of researches about deep learning on graphs from my group and finally discuss about promising research directions.

报告人介绍：

Bio: Jiliang Tang is an assistant professor in the computer science and engineering department at Michigan State University since Fall@2016. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015. His research focuses on developing learning, mining and optimization algorithms to glean value in data from the graph perspective and their applications on social and education domains. He was the recipients of The NSF Career Award, The Best Paper Award in ASONAM2018, the Criteo Faculty Research Award 2018, the Best Student Paper Award in WSDM2018, the Best Paper Award in KDD2016, the runner up of the Best KDD Dissertation Award in 2015, and the best paper shortlist of WSDM2013. He has served as the editors and the organizers in prestigious journals (e.g., TKDD) and conferences (e.g., KDD, WSDM and SDM). He has filed more than 10 US patents and has published his research in highly ranked journals and top conference proceedings, which received more than 8000 citations with h-index 42 and extensive media coverage. More details can be found via https://www.cse.msu.edu/~tangjili/.

主办单位：

全国信誉最好的网投平台(中国)有限公司

公司计算机科学与技术学院

公司数学学院

公司通信工程学院