Deep Learning for Population Genetic Inference with Dan Schrider

Banner Image: Dan Schrider - Podcast Interview
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About this Episode

Today we're joined by Dan Schrider, assistant professor in the department of genetics at The University of North Carolina at Chapel Hill. My discussion with Dan starts with an overview of population genomics and from there digs into his application of machine learning in the field, allowing us to, for example, better understand population size changes and gene flow from DNA sequences. We then dig into Dan's paper "The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference," which was published in the Molecular Biology and Evolution journal, which examines the idea that CNNs are capable of outperforming expert-derived statistical methods for some key problems in the field.
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Thanks to our sponsor PegaSystems

Thanks to our friends at Pega for their support of the podcast and their sponsorship of today's show. Pega is a low-code platform for AI-powered decisioning and workflow automation. Its scalable architecture helps the world's leading organizations work smarter, unify experiences, and adapt instantly - so they're always ready for what's next. Check out the latest from Pega at their annual conference PegaWorld inspire, which will focus on how to address constantly shifting perspectives in an ever-evolving world, including guidance, strategies, and powerful tools to achieve resiliency in the face of rapid change. The event will be held virtually on May 24th for the Americas and Europe. And again, on May 25th for Asia Pacific. Agenda and registration details can be found at


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