Spectral Method: Old Wine in the New Bottle

主讲人：王婉洁

Dr. Wanjie Wang is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS). She earned her Ph.D. in Statistics from Carnegie Mellon University in 2014. Following her doctoral studies, she completed a two-year postdoctoral fellowship in Statistics and Biostatistics at the University of Pennsylvania before joining NUS in 2016.

Dr. Wang’s research focuses on high-dimensional statistics, social network analysis, and spectral methods, with applications spanning psychology, genetics, and genomics. Her work aims to bridge theoretical advancements with practical solutions to address complex challenges in these interdisciplinary fields.

1  报告信息

时间 

2025年10月22日（周三）

10:00-11:00

地点 

中国人民大学

明德主楼1016会议室

2  报告摘要

In the era of AI, we meet more and more complex data: data with different format, different sources, and different intrinsic structures. The simple linear assumption does not hold anymore. However, the PCA method is still employed as a pre-processing step for these complex data, with impressive performance. It causes a question: why the spectral method still works?

In this talk, I will briefly introduce our work using the spectral information in three aspects: the manifold data, the network and covariate integration, and the bipartite networks from multiple sources. The analyzing tools are different in these applications, but all with good results and efficient calculation.

Our work shows that the spectral information is useful not only because of its long history, but also its beautiful theoretical properties.