DATA INSIGHT
LIVE WEBINAR
Experiment faster and more efficiently with Bayesian optimization
Date: 5 March
Time: 11:00 CET | 10:00 GMT
Duration: 30 minutes
Registration: FREE
An unpredictable number of experiments, wasted budget and time, and missed optimization targets place significant pressure on R&D groups as they work to move faster, increase capacity, and remain competitive. Bayesian optimization offers a modern approach to experimentation, using prior information - such as historical data, initial experiments, or partial results - to intelligently select the next best experiments to run.
Using Bayesian optimization means fewer trials, more information from each run, and a faster path to true understanding; for scientists and engineers, it means quicker insights, better-informed decisions, and progress with less trial and error.
Join this webinar to learn how Bayesian optimization can help speed experimentation and improve outcomes across your organization.
Key takeaways:
- How Bayesian optimization uses existing data to guide smarter experiment selection.
- How to reduce the number of experimental runs while gaining more insight per test.
- How to achieve optimization more quickly, even with limited time and resources.
About the Presenter
Ben Barroso-Ingham
Ben Barroso-Ingham is a Systems Engineer at JMP. Previously, he has held roles as a Fermentation/Upstream Scientist at Elanco Animal Health and Allergan Biologics, focusing on developing small-scale microbial fermentation models and the application of design of experiments.
He has a Ph.D. in chemical engineering from the University of Manchester, where he focused on the use of DOE in fermentation optimization and analytical method development.