“Identifiable Deep Generative Models via Sparse Decoding”, presented by Gemma Moran, Postdoctoral Research Scientist, Data Science Institute, Columbia University.

“Harnessing Extra Randomness: Replicability, Flexibility and Causality”, presented by Richard Guo, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge.

“Optimal Subgroup Identification”, presented by Matteo Bonvini, Department of Statistics and Data Science, Carnegie Mellon University.

“Bayesian Spatial Homogeneity Learning for Functional Data”, presented by Guanyu Hu, Department of Statistics, University of Missouri.

“The Promises of Parallel Outcomes”, presented by Ying Zhou, Department of Statistics, University of Toronto.

“3D Bivariate Spatial Modelling of Argo Ocean Temperature and Salinity Profiles”, presented by Mary Lai Salvana, Postdoctoral Fellow, Department of Mathematics, University of Houston.

“Gaussian Processes as a Tool to Represent Complex Phenomena”, presented by Marcin Jurek, Department of Statistics and Data Sciences, University of Texas at Austin.

“Causal Inference for Environmental Health Data: Estimating Causal Effects in the Presence of Spatial Interference”, presented by Nathan Wikle, Department of Statistics and Data Sciences, University of Texas at Austin.

“Causal Inference for Environmental Health Data: Estimating Causal Effects in the Presence of Spatial Interference”, presented by Nathan Wikle, Department of Statistics and Data Sciences, University of Texas at Austin.

“Non-Technical Strategies and Behaviors Every Successful Data Analyst Should Adopt”, presented by Steve Leeds, Vice President of Business Analytics, Ironwood Pharmaceuticals