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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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