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Dmitry Kobak

@hippopedoid.bsky.social
324 followers 67 following 18 posts

PI at Ghent University and VIB.AI. Manifold learning, contrastive learning, self-supervised learning, genomics, transcriptomics, interpretability. Excess mortality, statistical forensics. Born but to die and reas'ning but to err. dkobak.github.io

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Reposted by Dmitry Kobak
Greg Egan @gregegansf.bsky.social · 27/07/2026
While the Fields Medal celebrates the under-40s, the mathematician Joan Birman has, at the age of 99, solved a major open problem in representations of the Braid groups, a topic she has worked on for more than 60 years.
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VIB.AI @vibai.bsky.social · 30/06/2026
The @steinaerts.bsky.social lab received an ERC Proof of Concept Grant to develop CellTuned: an AI platform that combines sequence-to-function models with single-cell regulatory networks to uncover the mechanisms driving disease, and translate them into new therapeutic opportunities. 👏 celltuned.ai
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Dr Michele V @micheleveldsman.bsky.social · 25/06/2026
“I have done all of the things that one is supposed to do to earn a tenure-track position. And I have done approximately 85% of them by typing prompts into a large language model and then moderately editing the output.” 🤣🤣🤣 brilliant 👌🏽 open.substack.com/pub/inprepar...
open.substack.com
Opinion: I Was Not Allowed To Type Prompts Into ChatGPT During My Chalk Talk And This Is Discrimination
By Dr. Rachel Simmons, Postdoctoral Fellow, Stanford University
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Reposted by Dmitry Kobak
Vincent D. Warmerdam @koaning.bsky.social · 22/06/2026
@hippopedoid.bsky.social I *think* I'm speaking to the correct Dmitry when I say this, but I really liked your paper! So we made a highlight of it for our YT channel. youtu.be/2eoOqqLvkl8
youtu.be
MNIST is Better in One Dimension
YouTube video by marimo
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Dmitry Kobak @hippopedoid.bsky.social · 16/06/2026
A slide I prepared for my introductory departmental seminar last week to illustrate my CV as a weighted graph. Node weights (shown as marker areas) are the number of years I lived in each place: 24, 3, 2, 4, 9, 0. Edge weights (not shown) are the number of people moving each time: 1, 2, 4, 5, 5.
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Sasha Gusev @sashagusevposts.bsky.social · 23/05/2026
I assigned random gender/ethnicity labels to scientific abstracts from the literature and then asked Claude to do a thematic analysis. Claude identified a clinical versus computational split for female/male authors and a DEI focus for Black/URM authors. All in completely random data.
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Jan Lause @janlause.bsky.social · 09/06/2026
Meet Eyewire II: a new connectomic resource for the mouse retina. ~1 mm² of retina at nanometer resolution, with synapses and circuits of ca. 100,000 neurons, plus visual responses from the ~400 neurons shown in the video! Preprint: doi.org/10.64898/202... Data: eyewire.ai 🧠📈 🧠💻🧪 #VisionScience
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John C. Baez @johncarlosbaez.bsky.social · 09/06/2026
"An Erdős problem resolved by humans! One Abel prize winner was quoted as saying 'We knew the day would eventually come when humans could resolve Erdős problems, but we didn't know it would come this soon!' Several math departments now have plans for workshops on Human Alignment."
blog.computationalcomplexity.org
Humans Solve Erdos Problem!!
(In 2008 I wrote a survey of some of the known sum-product theorems, see  here . Avi Wigderson has a great slide-set on sum-product theorems...
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Dmitry Kobak @hippopedoid.bsky.social · 22/05/2026
Top-3 questions this month on MathOverflow are all about the math performance of frontier reasoning LLMs and what it implies for mathematical training and careers. Very interesting discussions with some very polarizing answers.
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Timothy Gowers @wtgowers.bsky.social · 20/05/2026
OpenAI's claim that this is a central conjecture in discrete geometry is not an exaggeration. This will I think be looked back on as the first time that AI solved a major mathematics problem (defined as a problem that all experts in some subfield had thought about). openai.com/index/model-...
openai.com
An OpenAI model has disproved a central conjecture in discrete geometry
An OpenAI model solved the 80-year-old unit distance problem, disproving a major conjecture in discrete geometry and marking a milestone in AI-driven mathematics.
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Dmitry Kobak @hippopedoid.bsky.social · 18/05/2026
This is a very cool work! Only saw it now. They could extract almost the entire (!) The Great Gastsby and 1984 from Claude (with some jailbreaking). But for some reason not Catch-22. I wonder why that is and what it tells us about the training data (or about Catch-22?). Catch-22 is great btw.
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Dmitry Kobak @hippopedoid.bsky.social · 15/05/2026
I am in the process of moving from Tübingen to Ghent, where I joined UGent and @vibai.bsky.social. Am really looking forward to working with wonderful VIB.AI colleagues @steinaerts.bsky.social, @joanampereira.bsky.social, @ppjgoncalves.bsky.social, @wsaelens.bsky.social. The lab is hiring!
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VIB.AI @vibai.bsky.social · 18/03/2026
We are super excited to welcome Dmitry Kobak @hippopedoid.bsky.social as a new VIB.AI group leader! The Kobak lab will officially kick off in April at the VIB.AI Ghent hub. More: vib.ai/en/news/dmit...
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Dmitry Kobak @hippopedoid.bsky.social · 27/08/2025
We spent a year writing this review of low-dim embeddings and arguing about things like epistemic roles and best practices :-) 20+ authors are all participants of the Dagstuhl seminar we held last year: www.dagstuhl.de/24122. Led by @alexandr.bsky.social and Cyril de Bodt. arxiv.org/abs/2508.15929
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Alex Diaz-Papkovich @alexandr.bsky.social · 27/08/2025
one of the more fun topics stemmed from a discussion with @hippopedoid.bsky.social over what the oldest 2D PCA visualization we could find was. After some scouring, we settled on a 1960 paper from researchers at the Université de Montréal about turtle carapaces, which we recreated.
Recreated PCA figure from: P. Jolicoeur and J. E. Mosimann. Size and shape variation in the painted turtle. A principal component analysis.
Growth, 24:339–354, 1960.

The figures show PC1 vs PC2 and PC2 vs PC3, with colours and symbols reflecting the sex of the turtle.
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Alex Diaz-Papkovich @alexandr.bsky.social · 27/08/2025
Last year I met a bunch of great researchers who work with high-dimensional data at a Dagstuhl seminar. This week we put out a preprint about the history and philosophy of low-dimensional embedding methods, their applications, their challenges, and their possible future arxiv.org/abs/2508.15929
The participants of Dagstuhl Seminar 24122 standing on steps outside (from https://www.dagstuhl.de/24122)Multiple types of embeddings (UMAP, t-SNE, Laplacian Eigenmaps, PHATE, PCA, MDS) of Wikipedia text data labelled by a text summaries generated by an LLM. Methods like UMAP and t-SNE show cluster structure that reflect shared subject matter in text, whiel other methods show more continuous structure.Multiple embedding methods (PCA, Laplacian Eigenmaps, t-SNE, MDS, PHATE, UMAP) of primate brain organoids at different time periods. Different methods highlight different aspects of development, such as clusters of similar cell types or time courses of cell development.Multiple embedding methods (PCA, Laplacian Eigenmaps, t-SNE, MDS, PHATE, UMAP) of 1000 Genomes Project genotypes. Different methods reflect different aspects of demographic history of populations.
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