@jacobbamb

Looking for geometry where it shouldn’t be. PhD student @UniofOxford. Interested in Geometric Deep Learning

jacobbamb.github.io
Joined June 2020
  • Tweets157
  • Following780
  • Followers555
11Media
Finally getting around to sharing our Riemannian Metric Matching paper! We presented it as an oral at ICML this summer, and it also received the Best Short Paper Award at the ICLR GRAM workshop 🎉
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Kieran Didi
@DidiKieranOct 21
Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 rfd4-proteina.org Presenting RFD4-Proteina, our latest protein design model, significantly better and faster than everything we compared against, both in silico and in the lab! 1/30
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Joey Bose
@bose_joeyOct 19
🚨 New Paper Alert, 🎤 Accepted for an oral presentation at the NeurIPS 2026 STODY workshop! 📣 Introducing: Normalizing Flow Maps (NFM): Combining few-step transport from Flow maps with tractable likelihoods from Normalizing Flows. This is my first work with my first crop of new PhD students, @francescoshox and @tim_hitge, who genuinely knocked this project out of the park. 📄 Paper: arxiv.org/abs/2610.22711 📝 Blog: the-bose-lab.github.io/norma… 💻 Code: github.com/francescoshox/Nor… 🧵 1/8
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Oscar Davis
@osclsdOct 14
Continuous Diffusion LMs went from niche to contender this year, but the recipe is shaky: why self-conditioning? How to embed tokens? 🤨 Our new work takes your one-hot flow from 4% to 50% on TinyGSM without self-conditioning, and explores the design space of CDLMs. 🔥🚀 (1/6)
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Katarina Petrovic
@kpetrovvicOct 13
New preprint: 🚀 Global Transport (GT) couplings for classifier-free guided flows! Surprisingly, a simple label-free optimal transport coupling beats both class-conditional OT and independent couplings once we push the CFG scale up 🧗 reaching 1.91 FID on ImageNet-256
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Michael Bronstein@mmbronsteinOct 6
youtu.be/DImSHPyUYHc

Michael Bronstein - Mathematical Foundations of AI Conference 2026

Michael Bronstein | Mathematical Foundations of Artificial Intellig...

youtube.com
Michael Bronstein@mmbronsteinSep 3
It was a pleasure to present the recent works on diffusion geometry with @jacobbamb @iolo_jones @AGosztolai @trekkinglemon at the Erlangen AI Hub conference in Oxford. Predictably, there were horses 🐎
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Lazar Atanackovic@lazar_atanSep 24
We’re in! See you @NeurIPSConf! (thread coming soon) arxiv.org/abs/2605.28075

Measure-to-measure Regression with Transformers

Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a probability measure, with point clouds as a key...

arxiv.org
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😱 A #neurosymbolic paper as a spotlight at #NeurIPS!?! 😱 We must have done something right to please the #ML gods, @GdiGiotto @lohithkonathala! 🙏 Stay tuned to find out how we get scalable and guaranteed constraint satisfaction 👀
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Michael Bronstein@mmbronsteinSep 19
Make geometry great again
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Kieran Didi
@DidiKieranSep 18
Our wet-lab validation campaign for Proteina-Complexa is now on bioRxiv! It includes some new exciting experimental results, from large protein structures (fully codesigned!) to functional carbohydrate binders. biorxiv.org/content/10.64898… Thread with some of the additions 🧵1/n

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules,...

biorxiv.org
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Jacob Bamberger@jacobbambSep 15
Finally getting around to sharing our Riemannian Metric Matching paper! We presented it as an oral at ICML this summer, and it also received the Best Short Paper Award at the ICLR GRAM workshop 🎉
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Iolo Jones@iolo_jonesSep 15
Kernel/graph based geometry can only go so far and it’s a big challenge to replace it with deep learning. We’re so excited to share this step towards neural diffusion geometry! Plus lots more to come.
Jacob Bamberger@jacobbambSep 15
Finally getting around to sharing our Riemannian Metric Matching paper! We presented it as an oral at ICML this summer, and it also received the Best Short Paper Award at the ICLR GRAM workshop 🎉
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For me this is a step towards Neural Diffusion Geometry: a principled yet scalable approach to learning the intrinsic geometry of data.
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We’ve also put together code, pretrained models and tutorial notebooks 👇 Joint work with @AGosztolai , @trekkinglemon, @mmbronstein, @iolo_jones 📰 Paper: arxiv.org/abs/2606.14334 💻Code: github.com/jacobbamb/metric-…

Riemannian Metric Matching for Scalable Geometric Modeling of Distributions

High-dimensional datasets often concentrate near low-dimensional structures, but estimating their geometry from samples typically relies on graphs and kernels that scale poorly with dataset size...

arxiv.org
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Michael Bronstein@mmbronsteinSep 3
It was a pleasure to present the recent works on diffusion geometry with @jacobbamb @iolo_jones @AGosztolai @trekkinglemon at the Erlangen AI Hub conference in Oxford. Predictably, there were horses 🐎
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Max Welling@wellingmaxAug 6
My new book written with @sirui_lu97 and @HoldijkLars is ready to be ordered! Major societal and economic shifts have always been anchored in foundational physics. During the 1st Industrial Revolution, thermodynamics showed us how to harness energy to power steam engines.
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Xingyue Huang@hxyscottMay 7
Now that the NeurIPS deadline is over, consider submitting your work to the ICML 2026 Workshop on Graph Foundation Models (GFM)! We would love to see more exciting work from the community! 🗓️ Submission Deadline: May 8, 2026 AoE
Xingyue Huang@hxyscottMay 1
📢 Deadline Extension Announcement — ICML 2026 GFM Workshop To better accommodate the NeurIPS submission timeline, we have extended the submission deadline. 🗓️ New Deadline: May 8, 2026 We hope this extension provides additional flexibility for authors.
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Michael Bronstein@mmbronsteinMay 7
Multiple postdoc positions in geometric ML and generative modeling are available at Oxford in collaboration with Aithyra and Imperial @bose_joey lnkd.in/eCMYpxzT
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