Invited Sessions:

1.Tradeoffs Between Computational Costs and Statistical Efficiency (Session organizer: Min Yang, U of Illinois at Chicago)

• William Li, Shanghai Advanced Institute of Finance. On optimal designs in information-based optimal subdata - A systematic view of a data reduction strategy with application to second-order model.

• Yanxi Liu, U of Illinois at Chicago. Information-based Optimal Subdata Selection for Clusterwise Linear Regression

• V. Roshan Joseph, Georgia Tech. Supervised compression of big data

2. Optimal Designs (Session organizer: John Stufken, UNC Greensboro)

• Jesús López Fidalgo, University of Navarra. Active Learning considering the marginal distribution of the covariates

• Kalliopi Mylona, King's College London. Optimal split-plot designs for precise pure-error estimation of the variance components

• Rakhi Singh, UNC Greensboro. Design selection for 2-level supersaturated designs

3. New Developments in Factorial Designs and Orthogonal Arrays (Session organizer: Hongquan Xu, UCLA)

• Jessica Jaynes, California State University, Fullerton. Orthogonal Array Composite Designs for Drug Combination Experiments with Applications for Tuberculosis

• Robert Mee, University of Tennessee. Two-level parallel flats designs

• Lin Wang, George Washington University. Orthogonal subsampling for big data linear regression

4. Industry experiments or internet experiments (Session organizer: David Steinberg, Tel Aviv University )

• Susan Murphy, Harvard. Micro-Randomized Trials & Online Decision-Making Algorithms Micro-Randomized Trials & Online Decision-Making Algorithms

• Julie Beckley, Etsy. Improving Internet Experiment Validity.

• Weitao Duan, LinkedIn. Experiment Velocity and Trustworthiness at LinkedIn

5. Bayesian Optimization and Active Learning (Session organizer: Robert Gramacy, Virginia Tech)

• Nathan Wycoff, now Virginia Tech soon to be Georgetown. Learning And Deploying Active Subspaces On Black Box Simulators

• Max Balandat, Facebook. Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces

• Matthias Poloczek , Amazon. Scalable High-dimensional Bayesian Optimization

6. Bayesian adaptive clinical trial designs: using uncertainty and information (Session organizer: Peter Mueller, U of Texas)

• Tianjian Zhou, CSU. Probability-of-Decision Designs to Accelerate Dose-Finding Trials

• Daniel Schwartz, U Chicago. Bayesian Uncertainty-Directed Designs with Model Averaging for Faster and More Informative Dose-Ranging Trials

• Meizi Liu, U Chicago. PoD-BIN: A Probability of Decision Bayesian Interval De- sign for Time-to-Event Dose-Finding Trials with Multiple Toxicity Grades

7. Analyzing Clinical Trials Disrupted by COVID (Session organizer: Nancy Flournoy, University of Missouri)

• Richard Emsley, King's College London. Frequentist and Bayesian approaches to rescuing disrupted trials

• Kelly Van Lancker, Ghent University. Potential estimands and estimators for clinical trials impacted by COVID-19

• Diane Uschner, George Washington University. Randomization tests to address disruptions inclinical trials

8. Bayesian design / model-robust designs (Session organizer: Dave Woods, University of Southampton)

• Tim Waite, University of Manchester, UK. Minimax efficient random experimental design strategies with application to model-robust design for prediction

• Lida Mavrogonatou, MRC Biostatistics Unit, University of Cambridge, UK. Optimal Bayesian experimental design for model selection through minimisation of f-divergences

• Lulu Kang, Illinois Institute of Technology, USA. A Maximin Φp-Efficient Design for Multivariate Generalized Linear Models