A Systematic Approach to Deliver Data Science Solutions for Bioprocess Development Leveraging Digital Infrastructure and Data Management Solutions

A streamlined approach to analytics can play a crucial role in optimally using digital infrastructure and data management solutions to realize the full potential of advanced analytics for process development. We will discuss our systematic approach to develop scalable advanced analytics solutions including multivariate statistics, digital twins based on AI/ML, mechanistic or hybrid approaches, and more.  Emphasis will be laid on the strategy to democratize and decentralize development efforts and empower citizen data scientists to use these methods to bring new medicines to patients. A diversity of skill sets required to deliver these solutions, including collaboration with our IT partners will be presented. We will also share our experience in employing cloud-based analytics platforms to build an eco-system of data science products that are widely accessible for end-user consumption as well as to inspire other potential citizen data scientists.

Presented by Shyam Mudiraj, Lead Data Scientist and Senior Staff Engineer, Preclinical Manufacturing and Process Development (PMPD) group at Regeneron Pharmaceuticals

Shyam Mudiraj is working as a Lead Data Scientist and Senior Staff Engineer in Preclinical Manufacturing and Process Development (PMPD) group at Regeneron Pharmaceuticals. In his current role, Shyam is driving advanced visualization, data science including AI/ML, and advanced process control efforts across PMPD department. Shyam holds a doctoral and master’s degree in chemical engineering from University of Florida and bachelor’s in chemical engineering from BITS Pilani, India. Prior to joining Regeneron, Shyam has worked in diverse industrial domains including advanced materials and sustainable energy solutions. Shyam has expertise in process and product development, mechanistic and empirical modeling, advanced process control, multivariate data analytics, machine learning methods that aid the development of innovative manufacturing processes.