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Unlocking Predictive Power with Predictum’s SVEM Add-In for JMP
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In science and engineering fields, obtaining accurate predictions for products and processes can be the difference between success and failure. This becomes even more critical when considering the inherent complexity and interaction effects of inputs, as one sees in creating biotechnology manufacturing processes.
Traditional modeling methods often fail to account for this complexity, leaving experts unable to fully explain observed behavior. That’s why Predictum developed Self-Validating Ensemble Modeling (SVEM): an easy-to-use methodology designed to produce more reliable, accurate predictive models, even when working with small experimental data.
In this webinar, Predictum’s Philip Ramsey shares how SVEM works, recent enhancements, and how it extends the power and ease of predictive modeling in JMP.
In this session, you will:
- Learn to build accurate, reliable predictive models that can handle complex science and engineering systems using the SVEM Methodology
- Identify why traditional data collection and modeling methods fail to accurately predict for complex systems
- Understand enhancements in SVEM 2.0, further extending power and ease of use
Presenters:
In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA.
Phil holds a Ph.D. in statistics from Virginia Polytechnic Institute and State University.
Webinar resources and contact info:
EPISODE LINKS:
Philip Ramsey, Ph.D.’s work profile: https://predictum.com/company/#leadership
Predictum SVEM Product Page: SVEM
SVEM Frequently Asked Questions: Frequently Asked Questions about SVEM
Free SVEM Micro-Course: https://courses.predictum.com/courses/build-better-and-faster-with-small-experimental-designs-and-machine-learning
PEER-REVIEWED RESEARCH ON SVEM:
- T. Lemkus, P.J. Ramsey, C. Gotwalt, and M. Weese, “Self-Validated Ensemble Models for Design of Experiments” (research paper, arXiv.org, Cornell University, 2021).
- Eliza Yeung and Philip J. Ramsey, “Optimization of a conventional glycosylation analytical method using machine learning and experimental design.” (research paper, BioProcess J, 2021; 20).
- Danial Mirzaiyanrajeh, Eshan V. Dave, Jo E. Sias, and Philip Ramsey. “Developing a prediction model for low-temperature fracture energy of asphalt mixtures using machine learning approach.” (research paper, International Journal of Pavement, 2022).
CONTACT:
Predictum Inc – Contact Predictum
SHARE FEEDBACK ON THIS WEBINAR:
Submit Feedback – Feedback via Microsoft Forms
SOCIAL LINKS:
LinkedIn (Philip Ramsey): https://linkedin.com/in/philip-ramsey-a175148/
LinkedIn (Nathaniel Leies): https://linkedin.com/in/nathanielleies
LinkedIn (Predictum Inc.): https://linkedin.com/company/predictum
Try Predictum SVEM for Free (30-Day Evaluation License)
Requires active JMP License. Try JMP for free at jmp.com.
