October 9th

Session 1 (9:15-10AM)

Assessing Predictive Capability for Binary Classification Models

Mindy Hotchkiss, Enquery Research LLC

Abstract: Coming Soon 

Optimal Design of Experiments for Powerful Equivalence Testing

Peter Goos, KU Leuven

Abstract: Coming soon

Can ChatGPT Think Like a Quality and Productivity Professional?

Jennifer Van Mullekom and Anne Driscoll, Virginia Tech

Abstract: Coming soon

Session 2 (10:30AM-12PM)

Monitoring Parametric, Nonparametric, and Semiparametric Linear Regression Models using a Multivariate CUSUM Bayesian Control Chart

Abdel-Salam G. Abdel-Salam, Qatar University

Abstract: Coming soon

To Drive Improvement: Act on Every Count

James Lucas, J. M. Lucas and Associates

Abstract: Coming soon

Vecchia Approximated Bayesian Heteroscedastic Gaussian Processes

Parul Vijay Patil, Virginia Tech

Abstract: Coming soon

Impact of the Choice of Hyper-parameters on Statistical Inference of SGD Estimates

Yeng Saanchi, JMP

Abstract: Coming soon

Screening Designs for Continuous and Categorical Factors

Ryan Lekivetz, JMP

Abstract: Coming soon

Rerandomization Algorithms for Optimal Designs of Network A/B Tests

Qiong Zhang, Clemson

Abstract: Coming soon

Session 3 (2-3:30PM)

Probability of Detection: Evaluating the Reliability of Nondestructive Inspection Systems

Christine Knott, Air Force Research Laboratory

Abstract: Coming soon

Experiment Design and Modeling for Nondestructive Evaluation at NASA

Peter A. Parker, NASA

Abstract: Coming soon

TBD

TBD

Abstract: TBD

Large Row-Constrained Supersaturated Designs for High-throughput Screening

Byran Smucker, Henry Ford Health

Abstract: Coming soon

Optimizing User Experience in Statistical Tools through Experimental Design

Jacob Rhyne and Mark Bailey, JMP

Abstract: Coming soon

 

October 10th

Session 4 (8-9:30AM)

A Case Study in Image Analysis for Engine Cleanliness

Quinn Frank, Lubrizol

Abstract: Coming soon

The Power of Foldover Designs

Jonathan Stallrich, North Carolina State University

Abstract: Coming soon

Applications of Weibull Based Parametric Regression for Survival Data

Chad Foster and Sarah Burke, GE Aerospace

Abstract: Coming soon

Identifying Prognostic Variables across Multiple Historical Clinical Trials

Xueying Liu, Virginia Tech

Abstract: Coming soon

Simulation-Based Approach to Designing Analytical Validation Studies to Assess the Stability of Biological Material

Dilsher Dhillon, Freenome

Abstract: Coming soon

Session 5 (10-11:30AM)

Designing Experiments to Identify Optimal Operation Conditions for a Dynamic Cloth Media Primary Wastewater Treatment System

Madison De Boer, Baylor University

Abstract: Coming soon

Microstructure-based Statistical Tests for Material State Comparison

Simon Mason, The Ohio State University

Abstract: Coming soon

Optimal Robust Designs with both centered and baseline factors

Xietao Zhou, King’s College London

Abstract: Coming soon

Robust Parameter Designs Constructed from Hadamard Matrices

Yingfu Li, University of Houston – Clear Lake

Abstract: Coming soon

Exploratory Image Data Analysis for Quality Improvement Hypothesis Generation

Theodore T. Allen, The Ohio State University

Abstract: Coming soon

Boundary peeling: An outlier detection method

Maria L. Weese, Miami University of Ohio

Abstract: Coming soon

Session 6 (1:30-3PM)

Integrating SPC, DOE, and AI/ML for Enhanced Quality

Daksha Chokshi, StatQualTech

Abstract: Coming soon 

Using Input-Varying Weights to Determine a Soft Changepoint in Mixture distributions

Di Michelson, Caleb King and Don McCormack, JMP

Abstract: Coming soon

Monotonic Warpings for Additive and Deep Gaussian Processes

Steven D. Barnett, Virginia Tech

Abstract: Coming soon

Deep Gaussian processes for estimation of failure probabilities in complex systems

Annie S. Booth, Virginia Tech

Abstract: Coming soon

Predictive Modeling for Patient Care using AI in a Secure Environment

Sunil Mathur, Boston Medical Center

Abstract: Coming soon

Dealing with Sample Bias—Alternative Approaches and the Fundamental Questions They Raise

Frederick W. Faltin, Virginia Tech

Abstract: Coming soon