Course Overview:

A digital twin is a virtual recapitulation of a physical entity or system that is created in real-time and continuously updated with data from the physical counterpart. Digital twins are typically used for monitoring, analysis, and optimization of real-world systems or objects, with a focus on providing insights, predictions, and decision support. Additionality, they are designed to enable bidirectional communication and feedback loop between the physical object or system and its virtual replica. The concept of the digital twin is ideally suited to addressing the development and deployment of analytically sophisticated real time process control and decision support systems in the manufacturing of complex biologic products, especially as related to the field of regenerative medicine therapeutics. Digital twin approaches to smart manufacturing will be critical in addressing manufacturing process optimization, resulting in reduce costs, improved quality, safety, and reduced time to market. Quality control strategies can be improved by proactively identify manufacturing problems before they occur. Use of digital twins can also support and ensure regulatory compliance by providing more complete data and transparency of manufacturing processes to regulators. Digital twins can also be taken advantage of to develop personalization of biologic therapeutic products thus increasing their value lowering cost of production and providing access to advanced therapies for more patients around the world.

Presenter Bio:

Dr. Wolff is an Advisory Industry Consultant for SAS Institute’s Global IoT Division. He has 30 years of experience in the health and life science industries as a scientist and analyst working in the U.S. and Europe.  Mark is recognized as an accomplished practitioner, thought leader and lecturer in the development and application of advanced and predictive analytics. His work focuses on the application of machine learning and artificial intelligence approaches to streaming sensor telemetry and unstructured data including natural language processing and computer vision in support of improving health outcomes, patient safety, and the design of intelligent, decision support systems for clinical development, care delivery and digital health initiatives. He is also involved in the development of next generation manufacturing quality analytics for the biopharmaceutical, regenerative medicine, and medical device industries. Prior to joining SAS, Mark held various research and leadership positions in academia, government, and industry. He holds a Bachelor of Science degree from Loyola University in Maryland, a Master of Science and a Doctorate in Toxicology from North Carolina State University and has an academic appointment as a Visiting Fellow at the University of Miami Institute for Data Science and Computing.

Join the IoT Community Masterclass Series!

IoT Community Masterclasses  are held in a virtual classroom environment, allowing you to attend in real-time or on-demand, delivering exclusive IoT content by industry-leading keynote speakers, domain specialists, and subject matter experts.

IoT Community Masterclasses include

• engaging panel topics and panelists,
• on-topic user centered content,
• interactive workshops and tutorials
• provide insight, results, and key takeaways.

Who should attend the IoT Community Masterclasses?

The IoT Community Masterclass series, is an extension to the IoT Slam – series of events, is where the new multi-disciplinary community is forming around the convergence of software and hardware for:

• Engineers
• Researchers
• Roboticists
• Makers
• Developers
• Designers
• Founders
• Innovators
• From, Industry, Enterprise, Government & Academia

Benefits Of Joining:

Joining the IoT Community Master Class Series provides you with:

• Learn different themes and outcomes
• Hear about the latest IoT and cloud products, services and solutions from multiple industry verticals.
• Validate you IoT Strategy, and plug in your team to execute
• All materials will be recorded for On-Demand Playback