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How should biologists deal with the massive amounts of population genetic data that are now routinely available? Will AIs make biologists obsolete?
In this episode, we talk with Andy Kern, an Associate Professor of Biology at the University of Oregon. Andy has spent much of his career applying machine learning methods in population genetics. We talk with him about the fundamental questions that population genetics aims to answer and about older theoretical and empirical approaches We then turn to the promise of machine learning methods, which are increasingly being used to estimate population genetic structure, patterns of migration, and the geographic origins of trafficked samples. These methods are powerful because they can leverage high dimensional genomic data. Andy also talks about the implications of AI and machine learning for the future of biology research.
Cover Art by Keating Shahmehri. Find a transcript of this episode at our website.
By Art Woods, Cameron Ghalambor, and Marty Martin4.6
136136 ratings
How should biologists deal with the massive amounts of population genetic data that are now routinely available? Will AIs make biologists obsolete?
In this episode, we talk with Andy Kern, an Associate Professor of Biology at the University of Oregon. Andy has spent much of his career applying machine learning methods in population genetics. We talk with him about the fundamental questions that population genetics aims to answer and about older theoretical and empirical approaches We then turn to the promise of machine learning methods, which are increasingly being used to estimate population genetic structure, patterns of migration, and the geographic origins of trafficked samples. These methods are powerful because they can leverage high dimensional genomic data. Andy also talks about the implications of AI and machine learning for the future of biology research.
Cover Art by Keating Shahmehri. Find a transcript of this episode at our website.

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