Rudy Morel
@rdMorel
AI&Science | Resarch Fellow at @FlatironCCM | Member of @PolymathicAI | ex PhD student at @ENS_Ulm
I will be attending #NeurIPS2024! Feel free to ping me or the @PolymathicAI team if you want to chat about The Well 🥤or MPP 🚤! And since you like eye-catchy scientific gifs, here is a Rayleigh-Bénard convection one 😁 🥤: arxiv.org/abs/2412.00568 🚤: arxiv.org/abs/2310.02994
What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with @ZKadkhodaie @EeroSimoncelli, we develop a novel energy-based model in order to answer these questions: 🧵
What can swimming bacteria teach us about how the ocean’s layers mix? @PolymathicAI recently released two massive datasets for training artificial intelligence models to tackle problems across scientific disciplines, available on @huggingface. Learn more: simonsfoundation.org/2024/12/02/new……
Congratulations to #FlatironCCM research scientist @JiequnH on being awarded @TheSIAMNews' 2025 SIAG/CSE Early Career Prize! Read more: simonsfoundation.org/2025/02/24/ccm… #math
📢Tell your friends who want to work on building large foundation models in astronomy: Application to our postdoctoral positions in Paris 🇫🇷 is closing in a few days!! The deadline is on Feb 14, 202 :)
You saw our AstroCLIP, and have heard of MultiModal Universe! Now, come build and scale Foundation models for Astrophysics with us at @PolymathicAI! Check this out: aas.org/jobregister/ad… Deadline: Feb 14, 2025!
Many exciting questions in ML&science right now! Check out our summer internships in the Center for Computational Mathematics at the Flatiron Institute @FlatironCCM @FlatironInst Location: Manhattan, New York Apply here: apply.interfolio.com/159678 Deadline: Jan. 15, 2025

Excited to be giving an invited talk at Foundation Models for Science workshop at Neurips in ~1.5 hours about @PolymathicAI ! Come join us at West Meeting Room 202-204! at 11:15am! 🔥 Thank you to the organizers for making this workshop possible! fm-science.github.io
🔥 @PolymathicAI presents the coolest (IMO) and most diverse fluid dynamics dataset The Well at #NeurIPS2024 ! Date: tomorrow (Thursday) Dec 12 Time: 11am-2pm (PST)! Where: West Ballroom A-D #5102 Led by @mikemccabe210 and @oharub !! Data + code: github.com/PolymathicAI/t……
Excited to be at NeurIPS this week! 🎉 I'm part of four exciting projects being presented: The Well & Multimodal Universe: massive, curated scientific datasets LaSR: LLM concept evolution for symbolic regression MPP: 0th gen @PolymathicAI All posters Wed/Thu - stop by! 👋
Very proud of what we accomplished with @PolymathicAI + MultiModal Universe Collaboration! 👉100 TBs of Astronomical data ⭐️🌟💫 👉From > 10 telescopes, over 20 modalities 👉All ML training ready This will be presented today (Wed) at 4:30pm-7:30pm (PST) at West Ballroom A-D…
🔥@PolymathicAI team will present Multiple Physics Pretraining (MPP) tomorrow from 11am to 2pm (PST) at East Exhibit Hall A-C #4100 🔥 at #NeurIPS2024! Learn how to pre-train on various incompressible fluids simulations and make the AI model predict what will happen to nearly…
Applications to our Research Fellow position at @FlatironCCM are closing soon on Dec 15! It's a great place for doing fundamental ML research with a lot of freedom in a great environment, in the heart of NYC. Apply here: apply.interfolio.com/155357
Neural networks trained on diverse tasks share foundational patterns, says CDS Faculty Fellow Florentin Guth (@FlorentinGuth) & @JohnsHopkins' Brice Ménard. Their findings shed light on transfer learning's success & neural networks' universal encodings. nyudatascience.medium.com/universal-buil…
Today along with my project co-lead @oharub and the team @PolymathicAI I'm excited to announce the release of the Well, a 15TB collection of 15+ datasets for physical simulation. Paper: openreview.net/pdf?id=00Sx577… Github: github.com/PolymathicAI/t…
🧵 Could this be the ImageNet moment for scientific AI? Today with @PolymathicAI and others we're releasing two massive datasets that span dozens of fields - from bacterial growth to supernova! We want this to enable multi-disciplinary foundation model research.