@geletavc

Computer scientist researching AI for population genetics, ancestry, and genetic genealogy. I work at Apple and prev. at Amazon, Dolby, Microsoft, Ancestry.

 San Francisco, CA
margaritageleta.github.io/
Joined January 2014
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Next week I will be at Montreal, CanadašŸ for the @GeneticsSociety meeting. I will be presenting PCLAI, our new method for continuous local ancestry inference, at my poster on Oct 21, 2:30-4:30pm EDT, and speaking at the HPRC workshop on Oct 24. biorxiv.org/content/10.64898ā€¦
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Here are the key figures for that. We think an ultra-voyaging culture originated out of remote French Polynesia (Mangareva) and settled both Easter Island (cool far south Pacific) and Hawaii (only Polynesian island in the northern hemisphere) with related individuals settling both far-flung places.
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Just the Editor-in-Chief of Nature advising scientists not to share their data and findings with external AI model companies.
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This is a super cool approach to measure the structure of genetic variation
Alexander G. Ioannidis
@alexGioannidisSep 17
Fst is a key genetic differentiation metric, but it emphasizes common variants over rare. In @ieeeICASSP out now @geletavc introduces Fq, which can be tuned across the allele freq spectrum. Fst & Shannon differentiation fall out of it as special cases. arxiv.org/abs/2511.03063
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Replying to @judham_canaan @ieeeICASSP @geletavc
For different q, Fq weights rare variants more (or less), so can focus more on them. For q=2, Fq is just Fst, which weights common more & for q=1 it is Shannon differentiation, up-weighting rare more than Fst. Fq gives a theoretical structure for connecting these & for weighting.
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ICLR 2027 has received more submissions than all previous years (2013ā€“2026) combined
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Super cool stuff. Iā€™ve had a strong intuition about the connection between FST and entropy / information for a long time, but didnā€™t know about Tsallis entropy. Reading this makes the connection even clearer and much cleaner
Alexander G. Ioannidis
@alexGioannidisSep 17
Fst is a key genetic differentiation metric, but it emphasizes common variants over rare. In @ieeeICASSP out now @geletavc introduces Fq, which can be tuned across the allele freq spectrum. Fst & Shannon differentiation fall out of it as special cases. arxiv.org/abs/2511.03063
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TMLR has faced a deluge of submissions, necessitating stricter desk rejection policies due to limited reviewer capacity Co-EiC Nihar Shah reached out to authors of 10 papers slated for desk reject. Could they answer questions about their *own* submission? medium.com/@TmlrOrg/asking-aā€¦

Asking Authors About Their Own Papers

By Nihar B. Shah

medium.com
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Her Tsallis-order q fixation index, Fq, measures the fraction of Tsallis q-entropy lost within subpopulations relative to the pooled population, placing familiar measures inside a single entropy family. ā€¢ q=2 recovers the classical heterozygosity-based Fst ā€¢ q=1 gives Shannon differentiation (i.e., normalized mutual information), the absolute q=1 form is mutual information between allele identity and population label Instead of looking at only one statistic, we can now examine a spectrum across q (and its slope). Within 1ā‰¤qā‰¤2, lower q gives more weight to rare variants; higher q increasingly emphasizes common variants. This can expose differentiation patterns that a single Fst value may obscure.
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Fst is a key genetic differentiation metric, but it emphasizes common variants over rare. In @ieeeICASSP out now @geletavc introduces Fq, which can be tuned across the allele freq spectrum. Fst & Shannon differentiation fall out of it as special cases. arxiv.org/abs/2511.03063
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peak apple season is upon us
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If anyone here is at EMBL's "Reconstructing the human past" in Heidelberg (near where Heidelbergensis fossils were found, fun fact) I'm going to be presenting this as a poster, and would love to chat about my preprint (or anything else ancient DNA related šŸ˜) #EESHuman
Daniel Tabin
@DanTabinAug 10
Very excited to present a preprint of the first chapter of my thesis, which focuses the end of the LGM (Ice age / Last Glacial maximum) in Europe, and specifically the post-LGM Balkans.
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Well, what did you expect? A stereotypical test (of course) will output stereotypical results. Correlation ~0% Sun's sign & psychometrics checks out. Astrology is an occult practice, most of it still buried in Chaldean texts. I'd be surprised if LLMs performed well.
David Shor
@davidshorSep 10
We cross-referenced the psychometric personality data of ~ 1 million people with their zodiac astrological signs: 1) There is a small but statistically significant effect of zodiac sign on personality controlling for demographic and attitudinal variables 2) These observed personality correlations correspond strongly with LLM-generated "stereotypes" of astrological associations with personality 3) This association of observed astrological effects with astrological "stereotypes" is mediated strongly in auxiliary surveys by how frequently people read horoscopes and generally subscribe to astrology 4) Zodiac effects among people who never read horoscopes are a quite precisely estimated zero, while effects among people who read horoscopes everyday are on the order of 0.2 sds - on par with or larger than most psychometric associations found in social science
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Not arguing here whether astrology works. But having an accurate account of what its methods involve would be good before deciding what a test represents. Start with Claudius Ptolemy's book Tetrabiblos (ApotelesmatikĆ”, II CE) who laid down the foundations of classic astrology.
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Staff Scientist position at University of Copenhagen. Come join our work on ancient environmental DNA! candidate.hr-manager.net/Appā€¦

Staff Scientist or Research Consultant in Bioinformatics at Globe Institute

candidate.hr-manager.net
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ok but what does my sexual orientation have to do with submitting a paper to science
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ARGformer is out in Bioinformatics! doi.org/10.1093/bioinformatiā€¦ Just as PCA produces a low-dimensional representation of population structure from genotypes, ARGformer can do this now for ARGs...a representation that comes now directly from inferred genealogical topologies & at a haplotype level across each individual's genome. We do this by turning local genealogical paths into tokens & learning dense embeddings, similar to language embeddings, to visualize, cluster & query. Code github.com/AI-sandbox/ARGforā€¦ Team @d_bonet, Cole Shanks, @marcal_cc, @jordiatjhu

ARGformer: learning on ancestral recombination graphs with transformers

AbstractMotivation. Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through

academic.oup.com
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The FDA just authorized the first Apple Vision Pro app for use during surgery. Strykerā€™s SportSuite Vision creates a virtual surgical cockpit, putting the live camera feed, CT scans, and planning tools in the surgeonā€™s field of view.
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Gnomix is published! nature.com/articles/s41467-0ā€¦ Still the most accurate & fastest local ancestry inference (LAI) method, bests Flare and others (Recomb-Mix & Orchestra) released since our preprint. Easy use github.com/AI-sandbox/gnomix Leads @HelgiHilmars @arvind0422 @BarrabesMiriam

Scalable high resolution ancestry deconvolution for genomic data

Nature Communications - The authors present Gnomix, a local ancestry framework that delivers leading accuracy across diverse admixed datasets on both whole-genome and array data with high...

nature.com
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New preprint! Investigative genetic genealogy has revolutionized forensic identification, with hundreds of cases solved. But for any given new case, how likely is genetic genealogy to succeed? We used a whole-country genealogy to find out! 1/11 biorxiv.org/content/10.64898ā€¦

Forensic investigative genetic genealogy match rate estimated from a nation-wide population register

Forensic investigative genetic genealogy (FIGG) is a revolutionary method in forensic genetics, whereby genetic relatives of an unknown target person are detected in direct-to-consumer genomic...

biorxiv.org
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