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• Causal Latent Space-Based Models for Scientific Learning

  • When
    9 Nov 2026
  •  
    10:00 AM
  • Location
    Online

  Topic: Causal Latent Space-Based Models for Scientific Learning

  Speaker: Joan Borràs-Ferrís 

  Date: November 9th at 10:00 a.m. ET 

  Abstract

   Industrial processes generate large amounts of data during everyday operation. These records capture variations in raw materials, operating conditions and product quality, offering opportunities to learn about the process. But turning that information into a clear path for solving complex industrial problems remains challenging. This webinar explores how latent variable models, particularly partial least squares (PLS) regression, can support scientific learning from historical production data typical in Industry 4.0. It introduces causal interpretation in the latent space, where proposed changes follow the correlation structure represented by the model, and considers its implications for process improvement. Building on this, the talk will then examine how this framework can support practical decisions: defining multivariate raw material specifications linked to final product quality, expanding these specifications by adjusting process conditions to accommodate greater raw material variability, and comparing suppliers through a latent space-based capability index. Industrial case studies will illustrate the practical use of these methods and the role of process knowledge throughout the analysis. The webinar will close with Dragonet, a software tool developed to make the methods easier to apply. 
  Bio: Joan Borràs-Ferrís is a researcher specializing in chemical engineering, applied statistics, and data-driven process modeling in digitalized industrial environments. He holds a PhD in Statistics and Optimization from the Universitat Politècnica de València (UPV) and has developed his career between UPV (Spain) and Université Laval (Canada). He is currently Co-founder and Chief Technology Officer (CTO) at Kensight, a UPV spin-off focused on developing data-driven solutions for industrial processes. In 2024, he received the ENBIS Young Statistician Award for his work in introducing innovative statistical methods and promoting their use in industrial practice. 

  
  

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• Testing the Prediction Profiler with Disallowed Combinations: A Statistical Engineering Case Study

  • When
    1 Dec 2026
  •  
    10:00 AM
  • Location
    Online

  Topic: Testing the Prediction Profiler with Disallowed Combinations: A Statistical Engineering Case Study

  Speaker: Yeng Saanchi

  Date: December 1st at 10:00 a.m. ET 

  Abstract

  Statistical software is increasingly used to support complex analytical decisions, yet validating that such software behaves correctly across the wide range of problems encountered by users presents a substantial challenge. This presentation describes a statistical engineering approach to validating a new capability in the JMP prediction profiler for handling design spaces constrained by disallowed combinations.
  The team adopted an experimental design approach to software testing, namely combinatorial testing. Strength-2 covering arrays were used to construct an efficient test suite that covered all two-way interactions among factors while keeping the number of test cases manageable. A novel aspect of the approach was combining the generation of synthetic data sets and the selection of profiler test cases as a single combinatorial testing problem. This allowed the team to systematically explore a diverse space of possible data characteristics while controlling the number of test cases to consider.  Equivalence partitioning was used to determine the levels of inputs with a wide range of possible values. The result was, in effect, a dataset of datasets designed to exercise the profiler enhancement across a wide variety of conditions.
  Particular attention was given to determining appropriate test oracles for the selected test cases to ensure that the software is working as intended. The presentation will show how design of experiments techniques can be adapted to software testing and other problems in which exhaustive testing is impractical. It will also demonstrate how statistical engineering can provide a framework for bringing statistical knowledge, software engineering, optimization, and other domain expertise together to solve complex validation problems. 

  Speaker Bio

  Yeng Saanchi is a Research Statistician Tester at JMP Statistical Discovery, LLC. She received her M.S. in Statistics from the University of Michigan, Ann Arbor, and her Ph.D. in Statistics from North Carolina State University. At JMP, her work focuses on the numerical verification and validation of statistical routines and methodologies implemented in software. Her research interests include stochastic optimization, applications of optimal experimental design to precision medicine, and statistical software validation. She also enjoys exploring new areas and finding opportunities to apply statistical thinking to challenging problems.

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• ISEA Short Course Recording

  • When
    31 Dec 2026
  • Location
    Online

  Register

  
  

  STATISTICAL ENGINEERING: PROBLEM FORMULATION FOR SUCCESSFUL DATA PROJECTS

  Register to watch recording.

  Don't start with the data. Start with the problem. Master the art of problem formulation in statistical practice.

  Many data science, analytics, and improvement projects fail—not because of poor statistical methods, but because the wrong problem was defined in the first place.

  This interactive short course introduces the principles of Statistical Engineering and teaches participants how to frame, structure, and formulate problems that lead to successful data-informed solutions.

  Who Should Attend?

  • Experienced statisticians and data scientists who want to enhance project results
  • Early-career professionals involved in data-driven decision making
  • Graduate students in statistics, data science, analytics, or related disciplines

  What You Will Learn

  Participants will learn how to:

  • Apply the Statistical Engineering problem-solving framework
  • Recognize strengths and weaknesses of problem statements
  • Formulate clear, actionable, and measurable problem statements
  • Avoid common project pitfalls and framing errors
  • Develop strategies for tackling large, complex, and unstructured problems

  Course Delivery Format

  The course is split into two online live sessions

  Session 1: September 10 at 10 am - 12 pm EST

  Session 2: September 24 at 10 am - 12 pm EST

  Each session will utilize interactive learning through:

  • Expert instruction
  • Individual reflection exercises
  • Group discussions
  • Real-world case studies
  • Practical problem-formulation activities

  

  Meet Your Course Instructors

  Roger W. Hoerl

  
  

  Roger W. Hoerl is Brate-Peschel Professor of Statistics at Union College, in Schenectady, NY. Previously, he led the Applied Statistics Lab at GE Global Research. Dr. Hoerl has been named a Fellow of the American Statistical Association and the American Society for Quality, and has been elected to the International Statistical Institute and International Academy for Quality.

  Caleb King

  
  

  Dr. Caleb King is a senior developer for the DOE & Reliability group at JMP Statistical Discovery LLC. He received his PhD in statistics from Virginia Tech. His research interests included design of experiments and reliability. Prior to joining JMP, he worked as a senior statistician at Sandia National Laboratories. Dr. King currently also serves as Past Chair for ISEA.

  Valeria Quevedo

  
  

  Dr. Quevedo is an Associate Professor and researcher at the University of Piura. She holds a Ph.D. and an M.S. in Statistics from Virginia Tech (USA) and an M.S. in Operations Research from the University of British Columbia (Canada). She has professional experience in research and consulting projects within the fields of statistics and operations research. She is the current Chair of the Board of the International Statistical Engineering Association (ISEA).

  Shilpa Gupta

  
  

  Shilpa Gupta is an award-winning engineering educator and former data science leader whose work focuses on applied AI, statistical engineering education, authentic assessment, and the responsible use of LLM-based tools in engineering curricula. Dr. Gupta is an Adjunct Professor of Industrial and Systems Engineering at San Jose State University. She holds a doctorate in Industrial Engineering from Arizona State University.

  Registration

  Course Fee: US$30

  Includes recording and course materials.

  For questions and general inquiry: info@isea-change.org

  
  

  
  

  
  

  
  

  
  
  

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