UPDATE | May 11, 2026
Enhancing bioprocess productivity through modern machine learning methods
Modern machine learning methods are reshaping how bioprocess data is analyzed, enabling stronger predictive performance and deeper process understanding to support productivity improvement.
In this webinar, Seongjin Kim of Samsung Biologics shares how advanced machine learning approaches can be applied to bioprocess datasets to generate actionable insights. The session highlights the use of XGBoost for predictive modeling, Bayesian optimization for efficient model tuning, and Shapley values as an explainable AI (XAI) method to identify the process variables most strongly associated with productivity outcomes.
Discover how data-driven modeling and explainable AI approaches can support more informed process optimization and enhance bioprocess productivity.
Modern machine learning methods are reshaping how bioprocess data is analyzed, enabling stronger predictive performance and deeper process understanding to support productivity improvement.
In this webinar, Seongjin Kim of Samsung Biologics shares how advanced machine learning approaches can be applied to bioprocess datasets to generate actionable insights. The session highlights the use of XGBoost for predictive modeling, Bayesian optimization for efficient model tuning, and Shapley values as an explainable AI (XAI) method to identify the process variables most strongly associated with productivity outcomes.
Discover how data-driven modeling and explainable AI approaches can support more informed process optimization and enhance bioprocess productivity.
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