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Very pleased to be starting today as a senior researcher Microsoft Research, working on deep learning for molecular modelling!

Attending NeurIPS this week, hit me up!

New internship openings at AI4Science Microsoft Research in Cambridge (UK), Amsterdam and Berlin! careers.microsoft.com/us/en/job/1497…

🤯🤯🤯 #NeurIPS2022

This is the paper I should have written. #NeurIPS2022 #workboat2022

Super exciting research engineering position on the interface between AI and material discovery Microsoft Research AI4Science!

Personal news: I have recently joined Microsoft Research AI4Science in Berlin. Very excited to work with Frank Noe and the fantastic team!

Do you want to obtain accurate, long-horizon predictions with neural PDE solvers? Introducing PDE-Refiner, a training process that improves rollout accuracies, offers uncertainty estimates and better data efficiency. 📜: arxiv.org/abs/2308.05732 🖥️: phlippe.github.io/PDERefiner/ 🧵1/9

Interested in accelerating scientific discovery with AI? Join our interdisciplinary global team! Two exciting opportunities for engineers: Amsterdam or Berlin: jobs.careers.microsoft.com/global/en/job/… Beijing: jobs.careers.microsoft.com/global/en/job/…

Sharing an early preprint of my Microsoft AI4Science summer internship project. We developed SE(3) flow matching for protein backbone generation. Compared to SE(3) diffusion, we find our method achieves higher designability, faster sampling, with a way simpler implementation. 1/8

[1/N] Generative AI has revolutionized how we create text and images. How about designing novel materials? We at Microsoft Research #AI4Science are thrilled to announce MatterGen: our generative model that enables broad property-guided materials design. 👇 arxiv.org/abs/2312.03687

[1/7] 🎉Our paper Time-lagged Information Bottleneck (T-IB) is in #ICLR2024! Kudos to Bas Veeling Ryota Tomioka Patrick Forré & MSR AI4Science! 📄arxiv.org/abs/2309.07200 T-IB maps complex dynamics to simple latent spaces for super fast, highly accurate simulations!

At #ICLR2024 presenting our latent protein simulation poster with the amazing Marco Federici! Tuesday at 10:45, Halle B #64 Also happy to discuss proteins sampling, latent sim., and graph contrastive rep. learning, hit me up. iclr.cc/virtual/2024/p…

Excited to introduce Aurora: a foundation model of the atmosphere.  In <1min, Aurora produces 5-day global air pollution predictions and 10-day high-resolution weather forecasts that outperform SOTA classical simulation tools and the best specialized deep learning models… 1/n

I'm super excited to finally share what we've been working on for a long time: Aurora, a foundation model of the atmosphere.

We introduce Geometry-Informed Neural Networks to train shape generative models without any data (!!), combining learning under constraints, neural fields as a suitable representation, and generating diverse solutions to under-determined problems: 🖥️: arturs-berzins.github.io/GINN/

We have a new distillation method that actually *improves* upon its teacher. Moment Matching distillation (arxiv.org/abs/2406.04103) creates fast stochastic samplers by matching data expectations between teacher and student. Work with Emiel Hoogeboom Jonathan Heek @tejmensin. 1/4

🚀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

Announcing our new model for materials! FlowMM... - Generates stable & novel materials efficiently - Predicts crystal structure accurately - Generalizes Riemannian Flow Matching to point clouds w/ periodic boundaries arxiv.org/abs/2406.04713 Ricky T. Q. Chen Anuroop Sriram Brandon Wood

Super excited to share that MatterGen is published in Nature!