Benjamin Espen
Director, Engineering & Data Science, Cell Therapy Avobis Bio
Benjamin Espen is an independent expert in quality engineering and data science with over 20 years of experience in the medical device and biotechnology industries, Benjamin has worked extensively across process development, risk management, and statistical and data-driven quality systems. Earlier in his career, he played a key role in the design and development of an endovascular implant for the treatment of abdominal aortic aneurysms. Benjamin holds a B.S. in Physics from Northern Arizona University and is an active contributor to industry conferences and technical guidance initiatives.
Seminars
Machine learning promises to revolutionize cell therapy process development, yet
practical implementation remains limited. This workshop bridges theory and practice
by showcasing real-world machine learning applications from companies successfully
deploying these tools. Explore how machine learning addresses donor variability,
predicts outcomes, enables real-time monitoring, and streamlines data – while learning
which approaches deliver results vs speculations.
Workshop highlights:
- Accelerate and optimize process development by examining successful machine learning implementations in process development through case studies demonstrating practical utility, including tools used, data requirements, and measurable workflow improvements
- Improve product consistency and manufacturing predictability by leveraging machine learning to predict donor variability impacts on manufacturing outcomes and identify patient-specific factors affecting product quality and comparability
- Enable predictive process control without major infrastructure changes by integrating real-time monitoring and AI analytics into existing GMP processes without complete system overhauls to enable predictive process control
- Strengthen data integrity and analytical capability by establishing streamlined, traceable data collection systems that support AI model development while safeguarding against human error and enabling cross-platform analysis
