
Pim de Haan
@pimdehaan
Machine learning at CuspAI, materials discovery for carbon capture.
@pimdh.bsky.social
ID: 32910051
18-04-2009 15:15:01
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🚀We're excited to emerge from stealth and announce our $30M Seed financing round. A huge thank you to our incredible investor group including Hoxton Ventures, Basis Set, Lightspeed, Northzone, LocalGlobe, Touring Capital, Giant Ventures, FJ Labs, Zero Prime VC & Tiferes



Coming to devour your 3D anatomies: 🥼LaB-GATr🐊, geometric algebra transformers for large, biomedical surface and volume meshes. Will be presented MICCAI and Gram Workshop. Joint work with Baris Imre, Pim de Haan and Jelmer Wolterink. (code) github.com/sukjulian/lab-…




Today, I've joined CuspAI! With simulation and machine learning, we're going discover new materials for carbon capture. I am so excited to work with Max Welling, Chad Edwards and the rest of this team on this super important mission! Also: we're hiring!

Very cool that our paper on imitation learning with confounders got published! Congrats to Risto Vuorio


Coming to NeurIPS: learning the symmetries of dynamical systems with Noether's theorem! Congrats to amazing first author Tycho van der Ouderaa



Another exciting day with Michael Albergo giving his perspective on generative model for physics, followed by Pim de Haan with exciting new work on CNFs for Gauge theories and Christopher Anders talking about a software package for flows in LFT! 👀🤩


We got some exciting new work out, led by the amazing Johann Brehmer. We find that equivariance matters at even scale! Not because of data-efficiency (augmentation solves that), but because of compute efficiency! Read the paper for the fine print.


Today at NeurIPS, we’ll be presenting our Noether's Razor paper! 📜✨ 📅 Today Fri, Dec 13 ⏰ 11 a.m. – 2 p.m. PST 📍 East Exhibit Hall A-C, #4710 (ALL the way in the back I believe!) w/ Mark van der Wilk Pim de Haan Come say hi! 👋

Our paper got a prize :) Cheers to lead author Johann Brehmer, and fellow co-authors Sönke Behrends, and Taco Cohen. Our results hint that yes, also at large scale of data and compute, if your data has symmetries, you might be better off building these into your network.

Congratulations to Johann Brehmer Pim de Haan Taco Cohen ! Thanks for saving equivariance.

Please follow me on Pim den Hartog.bsky.social Long overdue to leave this sinking ship