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.

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.

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

"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."

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.

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-...

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.

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!

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...

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

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.

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