SNAB 2021 Workshop on Statistical Network Analysis and Beyond
Saturday, January 16, 2021
Organizers: Jiashun Jin (Carnegie Mellon University) and Ji Zhu (University of Michigan)

  • Complex network data poses great challenges to statisticians, but it also provides great opportunities. This workshop aims to advance our understanding on how modern statistics may help reshape the research area of network analysis and related fields, with talks on topics such as social networks, neural networks, graphical models, and hypergraphs. The workshop seeks to bring together statisticians from different research areas and disseminate their recent research results. The workshop will also help increase interactions between statisticians and researchers in other areas of science and engineering.
  • A more detailed program with talk abstracts is available here.
  • If you wish to attend, please register for free here.
  • All times are EST.

January 16 (Saturday)

8:30-8:35am Opening remarks
8:35-9:10am Jianqing Fan (Princeton University)
Community network autoregression
9:10-9:45am Tracy Ke (Harvard University)
Counting cycles in networks
9:45-10:20am Peter Song (University of Michigan)
Causal network construction via directed acyclic mixed graphs
10:20-10:35am Break
10:35-11:10am Cun-Hui Zhang (Rutgers University)
Factor models for high-dimensional tensor time series
11:10-11:45am Karl Rohe (University of Wisconsin)
Vintage factor analysis with varimax performs statistical inference
11:45-12:20pm Zongming Ma (University of Pennsylvania)
Global and individualized community detection in inhomogeneous multilayer networks
12:20-1:20pm Break
1:20-1:55pm David Donoho (Stanford University)
Prevalence of neural collapse during the terminal phase of deep learning training
1:55-2:30pm Nynke Niezink (Carnegie Mellon University)
Bringing perspective into dynamic network analysis
2:30-3:05pm Arash Amini (UCLA)
Adjusted chi-square test for degree-corrected block models
3:05-3:20pm Break
3:20-3:55pm Xiaotong Shen (University of Minnesota)
Inference of causal relations with interventions
3:55-4:30pm Yingying Fan (University of Southern California)
Universal rank inference via residual subsampling with application to large networks
4:30-4:45pm Break
4:45-5:20pm Jun Liu (Harvard University)
Data splitting for graphical model selection With FDR control
5:20-5:55pm Annie Qu (University of California Irvine)
Community detection with dependent connectivity
5:55-6:00pm Closing remarks