Session: Industrial Data-Science Applications II
Process analytics in pharma
Salvador García Muñoz,Executive Director - Engineering, Eli Lilly
Sal Garcia Muñoz is a senior engineering advisor for Eli Lilly and Company, leading the efforts in building Digital Design capabilities in the small-molecule design and development organization at Eli Lilly. Prior to his current position, he spent nine years working for Pfizer Global R&D as a member of the process modeling and engineering technology group where he contributed to the scale-up and transfer of drug product manufacturing processes using modeling, simulation and data analytics. In his pre-pharma years, he worked for Aspen Technology as a business support engineer, providing consulting and services for the modeling and simulation and the real-time data management business.
He is a Visiting Professor in the Department of Chemical Engineering at Imperial College of London, a senior member of AICHE and an active member of AIChE's CAST Division. He was co-Meeting Program Chair for the AIChE Annual Meeting in Atlanta 2014 and was the co-founder of AIChE's Pharmaceutical Discovery Development and Manufacturing Forum (PD2M).
Next-generation data modeling for chemical manufacturing
Birgit "Bea" Braun, Sr. Research Scientist - Data Science and Hybrid Modeling, Dow
Dr. Birgit (Bea) Braun is a Senior Research Scientist aligned to the Machine Learning, Optimization and Statistics team in Core R&D at Dow Chemical focused on data science innovation. Her key interests are in using hybrid modeling and transfer learning to solve complex problems in chemical process applications and materials discovery. She is also passionate about applying data science and systems optimization thinking to sustainability challenges.
Bea started her Dow career in process-focused business R&D in 2011, where she quickly developed skills in the data science domain to address challenges she faced in her projects. In 2016, she moved to manufacturing focusing on using advanced data analytics and machine learning for plant troubleshooting and de-bottlenecking. She was a core team member for the corporate predictive maintenance strategy development and the implementation of Dow’s first deep learning-based image analysis. Bea joined Core R&D in 2021 spending more time on the front-end research for machine learning applications in industry and sustainability challenges.
Bea is a certified Six Sigma Black Belt, and has published 15 journal papers, more than 70 internal publications and presented her work at more than 20 conferences. She is the Section Chair for Analytics & AI at the AIChE Spring meeting, the incoming chair for the Industry 4.0 Topical at the AIChE Spring meeting for 2023, and an active member of the Site Implementation Leadership team for the Women’s Inclusion Network at Dow.
She previously worked at PolyNEW Inc. and VTU Engineering. She earned degrees in:
- PhD, Chemical Engineering, Colorado School of Mines
- MSc, Environmental Science and Engineering, Colorado School of Mines
- Dipl.-Ing., Process Engineering in Industrial Environmental Protection, Montanuniversität Leoben