- Genevera Allen (Rice University): Graph Learning for Functional Neuronal Connectivity
- Jim Berger (Duke University): The Many Facets of the Strawderman Prior
- Jianqing Fan (Princeton University): How Do Noise Tails Impact on Deep ReLU Networks?
- Dominique Fourdrinier (Université de Rouen, France): Data Based Loss Estimation of the Mean of a Spherical Distribution with a Residual Vector
- Ed George (University of Pennsylvania): From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction
- Ying Hung (Rutgers University): Functional-Input Gaussian Processes with Applications to Inverse Scattering Problems
- Iain Johnstone (Stanford University): Expectation Propagation in Mixed Models
- Eric Marchand (Université de Sherbrooke, Canada): The Search for Efficient Predictive Density Estimators
- Takeru Matsuda (RIKEN, Japan): Matrix Estimation by Singular Value Shrinkage
- Fatiha Mezoued (Ecole Nationale Supérieure de Statistique et d’Économie Appliquée, Algeria): Estimation of the Inverse Scatter Matrix for a Scale Mixture of Wishart Matrices Under Efron-MorrisType Losses
- Christian Robert (Université Paris-Dauphine, France): Bayesian Model Choice in Finite and Infinite Mixtures
- Andrew Rukhin (NIST): Heterogeneous Data and Objective Priors
- Robert Strawderman (University of Rochester): Robust Q-learning
- Larry Wasserman (Carnegie Mellon University): Causal Inference in the Time of Covid-19
- Marty Wells (Cornell University): On Graphical Models and Convex Geometry
- Emma Zhang (University of Miami): Network Community Detection: New Algorithms and Goodness-of-fit Tests
Final program, including all invited speakers, discussants, poster presenters will come soon.