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Transforming Rare Disease Research with Federated Data Models

About This Webinar

Sponsored by UBC & SymetryML

The rare disease registry landscape consists of small, disjointed data sets stored across different national registries and localized consortia. These data silos, coupled with strict data privacy regulations, limit the collaboration that is needed by researchers to enhance analysis.

In today’s webinar, we’ll show you how UBC has partnered with SymetryML and their unique Federated Learning 2.0 platform to overcome data silos and quickly & easily build machine learning models to improve research and outcomes.

Learning Objectives:
• Improving data sharing & collaboration in registries
• What is federated learning?
• How is SymetryML’s federated learning 2.0 different/better?
• Efficiently building and scaling machine learning models for enhanced analysis

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Who can view: Everyone
Webinar Price: Free
Featured Presenters
Webinar hosting presenter
VP of Peri- and Post-Approval Research, UBC
Mr. Berger joined the UBC team in 2003 and has since served in a range of diverse roles leveraging global capabilities in the execution of peri- and post-marketing product development programs and Real-World Evidence generation. Mr. Berger supports the development of RWD / RWE architectures and solutions to drive greater insight into the safety profile and value proposition of treatments.
Webinar hosting presenter
Co-Founder & CEO, SymetryML
Dustin leads all business development and partnerships at SymetryML. Dustin and his team have been building SymetryML over the last several years to help solve key industry problems with innovative AI & machine learning. Dustin has been entrepreneur for over 10 years and has had successful exits as an early employee and co-founder.
Attended (20)