• 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.