![[On Demand] Converging DOEs and Machine Learning with Self-Validating Ensemble Modeling](https://predictum.com/wp-content/uploads/2023/03/pexels-linkedin-sales-navigator-2182974-scaled.jpg)
Introducing Machine Learning Prediction for Small Experimental Data. Meet Self-Validating Ensemble Modeling (SVEM).
Click the image above for on-demand access to the webinar!
Obtaining accurate predictions for products and processes can be the difference between success and failure. For too long, science and engineering professionals have relied on outdated methods to make predictions via modeling.
In this talk, Chirs Gotwald, Chief Data Scientist at JMP Statistical Discovery and Philip Ramsey, VP of Data Science and Analytics at Predictum Inc. provide an overview of S-VEM as a new, advanced machine learning method, demonstrate a couple of use cases using our new S-VEM analytical product, and suggest how you can explore this method further.
Self-Validating Ensemble Modeling (S-VEM) is an exciting, new method that delivers machine learning accuracy to Design of Experiments (DOE) and has many applications in manufacturing and chemical processes.
Machine learning methods are valued because they produce excellent predictive models, but until now they have been disqualified from use with small data sets, including designed experiments, because of the limited amount of data.
The limitations have been:
- DoEs could not be validated, so they were less reliable.
- The relationship between the models and the designs were essentially fixed. Expanding the terms in the model and dealing with noisy data required running additional experiments.
- Typically experiments could only accommodate up to second-order parameters, yet the relationships among parameters is typically higher.
S-VEM generates robust, higher-order models that yield greater insights and characterization using considerably fewer runs, thereby saving time and money and reducing risk.
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Who Should Attend: Engineers, scientists and their management who are engaged in research, problem solving and process development and characterization
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
SOCIAL LINKS:
LinkedIn (Philip Ramsey): https://linkedin.com/in/philip-ramsey-a175148/
LinkedIn (Christopher Gotwalt): https://www.linkedin.com/in/chris-gotwalt-7ab0629/
LinkedIn (Predictum Inc.): https://linkedin.com/company/predictum
Try Predictum SVEM for Free (30-Day Evaluation License)
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