Alex Young: IQ, disease and statistical genomics
Razib Khan's Unsupervised Learning
Release Date: 12/09/2025
Chris Masterjohn: COVID-19 to mitochondrial health, communicating and applying "the science"
Razib Khan's Unsupervised Learning
Today, Razib talks to , a nutritional scientist and leading expert in mitochondrial biology who believes hidden energy bottlenecks underlie much of modern disease. After years of work as a professor and researcher, he founded , the first mitochondrial analysis designed for everyday health, and serves as its Scientific Director. His mission is to make mitochondrial testing accessible so people can identify and correct the specific energy limitations holding them back. After earning his PhD in Nutritional Sciences from the University of Connecticut in 2012, he completed a postdoctoral...
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Mike White: academia and genomics in the 21st century
Razib Khan's Unsupervised Learning
On this episode of Unsupervised Learning, Razib talks to , a Genetics professor at the Washington University in St. Louis. White has a position at the School of Medicine in St. Louis, where he leads a research team focused on understanding the biophysical architecture of regulatory DNA. He earned a B.A. in music before pivoting to the sciences, receiving his Ph.D. in Biochemistry from the University of Rochester in 2006 and completing a postdoctoral fellowship at Wash U under Dr. Barak Cohen. White’s work combines functional genomics, synthetic biology, computational biology, and deep...
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Aaron Renn: Heartland urbanism and leaving Left Behind behind
Razib Khan's Unsupervised Learning
On this episode of Unsupervised Learning Razib talks to . Renn is a , consultant, and known for his work on the challenges facing American cities and religious institutions in the 21st century. He is a contributor to and the author of , a book exploring the cultural shifts regarding Christianity in America. Renn previously served as a Senior Fellow at the Manhattan Institute for five years and as a contributing editor for City Journal, having established his voice on urban policy through his widely cited blog, . Prior to his career in...
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Daniel Tabin: ancient DNA, the good, bad and ugly
Razib Khan's Unsupervised Learning
On this episode of Unsupervised Learning Razib talks to , a 5th-year Ph.D. student in in the Department of Human Evolutionary Biology. His research focuses on using ancient and modern DNA to answer questions about human history. Tabin completed a degree in Computer Science and Math and Master’s in Computer Science from Rensselaer Polytechnic Institute. He Ph.D. project involves the population genetic history of Central and East Asia. First, Razib and Tabin discuss he co-authored that looks at problematic results in the paleogenetic literature due to contamination...
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Peter Nimitz: the story of the Slavs
Razib Khan's Unsupervised Learning
On this episode of Unsupervised Learning Razib talks to about . His Substack, titled , explores world history through the lenses of archaeology, paleogenetics, and historical processes. His writing focuses on "deep history," such as the Bronze Age Collapse and the migration of Indo-European peoples, while connecting these ancient shifts to broader patterns of civilizational rise and fall. Nimitz often integrates technical data from genetics and climate science to challenge traditional narratives about nomadic tribes and early state formations across Eurasia....
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Jesse Arm: Gen Z Republicans and their views
Razib Khan's Unsupervised Learning
On this episode Razib talks to , VP of external affairs at the Manhattan Institute. His writing and commentary have appeared in the New York Times, Wall Street Journal, The Atlantic, New York Post, Fox News, City Journal and Jerusalem Post. Arm graduated with honors from the University of Michigan, where he majored in international political economy, and studied language and international affairs at Tel Aviv University. He has also worked for Senator Tom Cotton and Representative Dan Benishek, and the analytics arm of American Continental Group, a major...
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Davide Piffer: how Europeans became white
Razib Khan's Unsupervised Learning
On this episode of Unsupervised Learning, Razib talks to , whose Substack examines genetic differences between populations. Piffer on human genetic variation for a decade, and recently started a Substack, , exploring similar issues in detail over a series of posts. Razib asks Piffer about the difficulties in analyzing polygenic scores from quantitative traits in ancient DNA samples. How does he do in technical terms, from genome quality to imputation to ancient populations from modern ones? Then, they discuss some of Piffer’s findings, in particular, his work...
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Aneil Mallavarapu: why machine intelligence will never be conscious
Razib Khan's Unsupervised Learning
Today Razib talks to , a scientist and technology leader based in Austin, Texas, whose career bridges the fields of biochemistry, systems biology, and software engineering. He earned his doctorate in Biochemistry and Cell Biology from the University of California, and has held academic positions at Harvard Medical School, where he contributed to the Department of Systems Biology and developed the "Little b" programming language. Mallavarapu has transitioned from academic research into the tech and venture capital sectors, co-founding ventures such as Precise.ly and DeepDialog, and...
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Richard Hanania: his break with the Right and the rise of kakistocracy
Razib Khan's Unsupervised Learning
On this episode Razib, talks to , a . Hanania holds a Ph.D. from UCLA, a J.D. from the University of Chicago, and an undergraduate degree from CU Boulder in linguistics. He is a regular contributor to the and , and has . Hanania is also the author of . Razib and Hanania talk about his new book , and his developing views on populism and immigration. They highlight the rise of populism on the Right, the rejection of cognitive elitism, and the impact of social media on political discourse. Hanania criticizes the far-right’s nativism,...
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Francis Young: Baltic paganism in modern times
Razib Khan's Unsupervised Learning
On this episode, Razib talks to ,, a historian who teaches at Oxford. Young specialises in the history of religion and belief from ancient times to the present day, and provides expert indexes for academic books and translates medieval and early modern Latin. He holds a PhD from Cambridge University and is the author, editor or co-author of . On his last visit to the , he discussed his book , an account of the practices and persistence of Baltic paganism down to the 16th-century, the age of the Renaissance and Reformation. Today he discusses his new book, . Razib and...
info_outlineThis week on the Unsupervised Learning Podcast, Razib talks to returning guest Alex Young of UCLA and Herasight. Trained originally as a mathematician, Young studied statistics and computational biology at the University of Cambridge before doing a doctorate in genomic medicine and statistics at the Wellcome Trust Centre for Human Genetics, University of Oxford, under Peter Donnelly. He also worked at deCODE Genetics in Reykjavik and at Oxford with Augustine Kong, developing methods in quantitative and population genetics.
Razib and Young talk extensively about what we know about heritability and genomics in 2025, four years after their first conversation. In particular, they discuss what larger sample sizes, high-density genotype-arrays and whole-genome sequencing have told us about heritability and the ability to predict traits in individuals from their sequence. They discuss quantitative and behavioral traits like height, intelligence and risk of autism, and the differences between classical statistical genetical methods utilizing twins and modern molecular genomic techniques that attempt to fix specific physical markers as causal factors in characteristics of interest. In addition to his academic work, Young has also been consulting for the polygenic embryo-screening company Herasight, working on cutting-edge methods for genomic prediction in the context of in vitro fertilization. They dig deep into the new method Young and colleagues worked on that helps democratize embryo selection using genomics, ImputePGTA.