Glen is a machine learning scientist who has worked in the healthcare, biomedical, and pharmaceutical industries since 2010. He joined industry full-time in 2018 after graduating from the Computational Health Informatics Lab (CHI) at the Oxford Institute of Biomedical Engineering (IBME). He primarily works on time-series data modeling for automated monitoring and anomaly/outlier detection for large continuous data streams. His research interests include Bayesian nonparametrics for personalized medical modeling and anomaly detection, the robust automation of statistical inference, and the experimental design of machine learning-based clinical trials. Outside of work he is active in several professional societies, including the American Statistical Association (ASA), the Institute of Engineering and Technology (IET), and the Institute of Electrical and Electronics Engineering (IEEE). Outside of nerd-work, he volunteers for his family’s non-profit, which organizes about 30 events per year to help other non-profits raise money for their charitable causes.
There are many places in which ML/AI methods can be of benefit to pharmaceutical research (several have already been covered on the show). David and Demissie explain where AI can fit in to in vivo studies, which carries it’s own benefits, but also with heightened risk to to human test subjects. They go on to cover several other areas of interest including AI for observation studies and real world evidence. It’s a “big tent” conversation as we lead up to the Pfizer/ASA/Columbia University Symposium on Risks and Opportunities of AI in Clinical Drug Development.Mihaela van der Schaar will be following up in a subsequent episode on this subject.
Dana al Sulaimen’s (MIT) work runs the gamut of biomedical engineering areas. She gives a great presentation on the clinical motivation for her work, engineering sensing platforms, and data analysis. Definitely watch the video for this one for some excellent visual material.
The episode of milk and honey.Shane shows us some of the real-time data analytics platforms that track the health of dairy cows and honey bees.
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Creator Details

Cary, North Carolina, United States of America
Episode Count
Podcast Count
Total Airtime
9 hours, 15 minutes