报告主题： The Power of Data: Transforming and Optimizing Data Representation Space

报告人： 傅衍杰

报告时间：2022年6月28日（周二）上午9：30-10：30

报告网址：腾讯会议：888-446-031

报告摘要： 

In the past years, research has been focusing on optimizing model space in AI. Deep learning models have successfully applied to almost every area.  Models trained with millions of parameters and sophisticated neural architectures are now used routinely.  It seems models play more role than data. We investigate a question: Can optimizing data space be as powerful as optimizing model space? Relevant representation learning techniques can automatically reconstruct data representation space.  But the techniques needs more explainable and traceable explicitness, and flexible optimal. In this talk, we will propose a concept of self-optimizing data geometry. We will introduce explainable and optimal representation space reconstruction from a selection perspective and a generation perspective. Finally, we will discuss our future work.

报告人简介：

Dr. Yanjie Fu is an assistant professor in the Department of Computer Science at the University of Central Florida. He received his Ph.D. degree from Rutgers, the State University of New Jersey in 2016, the B.E. degree from University of Science and Technology of China in 2008, and the M.E. degree from Chinese Academy of Sciences in 2011. His research interests include data mining and big data analytics. He has research experience in industry research labs, such as and IBM Thomas J. Watson Research Center and Microsoft Research Asia. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, IEEE TMC, ACM TKDD, ACM SIGKDD, AAAI, IJCAI, VLDB, WWW. He received US NSF CAREER Award (2021), ACM SIGSpatial Best Paper Runner-Up Award (2020), US NSF CRII Award (2018), ACM SIGKDD Best Student Paper Finalist (2018), University of Missouri System Research Board Award (2017), Microsoft Research Azure Research Award (2016), IEEE ICDM Best Paper Finalist (2014). He is committed to data science education. His graduated Ph.D. students have joined academia as tenure-track faculty members.