AI科学前沿讲堂
中国科学院人工智能产学研创新联盟标准组
北京理工大学计算机学院
共同主办
1 月 25日
10：00--11：00
第六讲
TOPIC
Challenges and Opportunities
Computer Science and Engineering Department
Michigan State University
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 at Yahoo Research. He got his Ph.D. from Arizona State University in 2015 and his MS and BE from Beijing Institute of Technology in 2010 and 2008, respectively.
His research interests include data mining and machine learning and their applications in social media and education. He was the recipient of 2020 SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, Aminer Influential Scholars in AI (2020, 2019), 2019 NSF Career Award, 2019 IJCAI Early Career Talk Award, and 7 best paper awards (or runner-ups) including WSDM2018 and KDD2016. His dissertation won the 2015 KDD Best Dissertation runner up and Dean's Dissertation Award. He serves as top data science conference organizers (e.g., KDD, SIGIR, WSDM, and SDM) and journal editors (e.g., TKDD and ACM Books). He has published his research in highly ranked journals and top conference proceedings, which received more than 13,900 citations with h-index 58 and extensive media coverage.
ABSTRCT
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 oppor-tunities and challenges, then share a series of researches about deep learning on graphs from my group and finally discuss promising research directions.
A comprehensive background of this research area can be found in recent book：
http://cse.msu.edu/~mayao4/dlg_book/
直播时间
2021年1月25日 周一
10：00--11：00
腾讯会议
ID：632 856 674
https://meeting.tencent.com/s/lXOmu1a3dGoF
或扫码入会，会议二维码：
