Schedule
Schedule
Day 1 (June 14)
Breakfast (7:30 - 8:25)
Introduction and Welcome (8:25 - 8:30)
8:30-9:00 Jianqing Fan, Princeton University
Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models
9:00-9:30 Pragya Sur, Harvard University
Spectrum-Aware Debiasing: Inference Beyond sub-Gaussian Covariates with Applications to Principal Components
Regression
9:30-10:00 Subhadeep Paul, The Ohio State University
Embedding Network Autoregression for Time Series Prediction and Causal Peer Influence Inference
10:20-10:50 Jiashun Jin, Carnegie Mellon University
Optimal Network Comparison
10:50-11:20 Patrick Rubin-Delanchy, University of Edinburgh
What Makes a Good Embedding?
11:20-11:50 Jiaming Xu, Duke University
Recent Advances on Random Graph Matching
14:00-14:30 Qiwei Yao, London School of Economics and Political Science
Autoregressive Networks with Dependent Edges
14:30-15:00 Tracy Ke, Harvard University
Testing High-dimensional Multinomials with Applications to Text Analysis
15:00-15:30 Joshua Cape, University of Wisconsin-Madison
Robust Spectral Clustering with Rank Statistics
15:50-16:20 Gongjun Xu,University of Michigan
Statistical Inference on Latent Space Models for Network Data
16:20-16:50 Yang Feng, New York University
Unsupervised Federated Learning: A Federated Gradient EM Algorithm for Heterogeneous Mixture Models with
Robustness against Adversarial Attacks
16:50-17:20 Elynn Chen, New York University
Semi-parametric Tensor Factor Analysis by Iteratively Projected Singular Value Decomposition
Day 2 (June 15)
Breakfast (7:30 - 8:30)
8:30-9:00 Weijie Su, University of Pennsylvania
How Statistics Can Advance Large Language Models: Watermarking and Fairness
9:00-9:30 Yingying Fan, University of Southern California
Robust Knockoffs Inference with Coupling
9:30-10:00 Eric Kolaczyk, McGill University
Causal Inference under Network Interference with Noise
10:20-10:50 Emma Zhang, Emory University
Preferential Latent Space Models for Networks with Textual Edges
10:50-11:20 Keith Levin, University of Wisconsin-Madison
Estimating Network-mediated Causal Effects via Spectral Embeddings
11:20-11:50 Bingyan Jiang, The Hong Kong Polytechnic University
A Two-way Heterogeneity Model for Dynamic Networks
13:40-14:10 Ji Zhu, Michigan University
A Latent Space Model for Hypergraphs with Diversity and Heterogeneous Popularity
14:10-14:40 Wen Zhou, Colorado State University
Informative Core and Partial Informative Periphery Detection in Weighted Directed Networks
15:00-15:30 David Donoho, Stanford University
TBD
15:30-16:00 Tianxi Li, University of Minnesota
The Non-overlapping Statistical Approximation to Overlapping Group Lasso
16:00-16:30 Yufeng Liu, University of North Carolina at Chapel Hill
Detecting Hub Variables in Large Gaussian Graphical Models
Banquet Dinner 18:30
Day 3 (June 16)
Breakfast (7:30 - 8:30)
8:30-9:00 Anru Zhang, Duke University
High-order Singular Value Decomposition in Tensor Analysis
9:00-9:30 Arian Maleki, Columbia University
Accurate and Efficient Data Removal in High-dimensional Settings
9:30-10:00 Kaizheng Wang, Columbia University
Transfer Learning for Contextual Network Analysis
10:20-10:50 Yuqi Gu, Columbia University
Locally Dependent Mixed Membership Estimation for High-dimensional Categorical Data
10:50-11:20 Claire Donnat, University of Chicago
Practical Canonical Correlation Analysis in High-Dimensions
11:20-11:50 Xiucai Ding, UC Davis
Eigenvector Distributions of General Large Sample Covariance Matrices with Applications
13:40-14:10 Jesus Arroyo, Texas A&M University
Learning Joint and Individual Structure in Network Data with Covariates
14:10-14:40 Lihua Lei, Stanford University
Causal Clustering: Design of Cluster Experiments under Network Interference
14:40-15:10 Jian Kang, University of Michigan
Network Latent Source Separation
15:30-16:00 Subhabrata Sen, Harvard University
Fundamental Thresholds for Community Detection on Multiview Networks
16:00-16:30 Weijing Tang, Carnegie Mellon University
Nonparametric Inference for Balance in Signed Networks