Mario 🇺🇦 💙💛 🇮🇱 + Scientific Machine Learning @ScientificML
{ Visual & Tensor Computing; Grad student in ML4Science; Vision, Deep Neural Networks, Optimization, and SciML Simulation; Python/NumPy, PyTorch, JAX, *nix,git} github.com/kmario23 Mars Joined February 2010-
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In a recent interview I talk about what it takes for AI to make new scientific discoveries. tldr: it won’t be just LLMs. newindiaabroad.com/english/techno…
More details can be found on the project web page: kmario23.github.io/loglo-fno
Please join this timely workshop on Multi-scale Modeling for (Physical & Chemical) Sciences and Engineering problems. We propose LOGLO-FNO, an improvement to the popular FNO arch, specifically targeted towards modeling high frequencies in turbulence simulations. #ai4science #pde
Poster: iclr.cc/virtual/2025/p… Hall 3 + Hall 2B #32, 10am Singapore time Paper: arxiv.org/abs/2408.01536
🚨ICLR poster in 1.5 hours, presented by Daniel Musekamp: Can active learning help to generate better datasets for neural PDE solvers? We introduce a new benchmark to find out! Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!
Thrilled to receive the outstanding paper award together with @Mangal_Prakash_ for our work on SE(3)-Hyena arxiv.org/abs/2407.01049 !🤘 Go long-convolutions!
Here's @erikjbekkers with the awardees.
📢 Come by our #ICML2024 poster "Neural Operators with Localized Integral and Differential Kernels"! 🚀 We propose two extensions of standard convolutional layers to operator learning! Location: Poster #216, 11:30 - 13:00 CEST @AnimaAnandkumar @julberner @Azizzadenesheli
The WaterLily.jl preprint is now available on arXiv! 📰 arxiv.org/abs/2407.16032 WaterLily is a #CFD solver written in the #JuliaLang for incompressible flow and moving bodies. Together with @gabrielweymouth, we provide the main numerical methods, the design behind the 1/2
Please come to the poster and chat with Jan and @ScientificML Mario!
📢#AI4Science Vectorized Conditional Neural Field (VCNeF) for Time-dependent Parameterized Partial Differential Equations at #ICML2024. VCNeF marries Neural Operators & Neural Fields. Project: jhagnberger.github.io/vectorized-con… Code: github.com/jhagnberger/vc… Paper: arxiv.org/abs/2406.03919
The proposed VCNeF architecture marries the two worlds, #NeuralOperators & #NeuralFields, and outperforms several SOTA methods such as Transformers, Neural Fields, Graph Neural Networks, and Fourier Neural Operators for solving parametric PDEs, e.g. Compressible Navier-Stokes.
To develop a model that encompasses these ideal characteristics, we propose VCNeF. This linear transformer-based conditional neural field continuously solves PDEs in space and time, endowing the model with spatial and temporal zero-shot super-resolution capabilities.
📢#AI4Science Vectorized Conditional Neural Field (VCNeF) for Time-dependent Parameterized Partial Differential Equations at #ICML2024. VCNeF marries Neural Operators & Neural Fields. Project: jhagnberger.github.io/vectorized-con… Code: github.com/jhagnberger/vc… Paper: arxiv.org/abs/2406.03919
“Starship is the kind of thing that makes people from little kids to seniors excited about the future.” — Elon Musk
@KyleCranmer @jmtomczak One of the amazing human beings I've met!! 😊
Super excited to share that I successfully defended my PhD thesis "Understanding Generalization and Robustness in Modern Deep Learning" today 👨🎓 A huge thanks to the thesis examiners @SebastienBubeck, @zicokolter, and @KrzakalaF, jury president Rachid Guerraoui, and, of course, Nicolas @tml_lab for all the supervision during these years! Seems like my 5-year journey at EPFL slowly comes to its end :-) Very excited for what comes next!
📢#AI4Science Talk on 12.02.24 at 15:00 (CET) / 09:00 (ET) on “Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths” by Lars Holdijk. Join us on Zoom if you're interested! Details: ai4sciencetalks.github.io/projects/pips_… #ML4science #MDSimulations
🚨Princeton ML Theory Summer School🚨 Aug 6 - 15, 2024 Speakers: * F. Krzakala/L. Zdeborova @KrzakalaF @zdeborova * S. Rakhlin * B. Lourerio @_brloureiro * M. Austern * D. Krotov @DimaKrotov * J. Altschuler Details: mlschool.princeton.edu Sponsors: @NSF and @Princeton: ORFE, @EPrinceton, PACM, CSML, @PrincetonPLI, PCTS Please consider retweeting: @boazbaraktcs, @pfau, @shaunmmaguire, @Andrea__M, @NSF, @prfsanjeevarora, @jasondeanlee, @ArthurJacot3, @CPehlevan, @HSompolinsky, @SoledadVillar5, @SebastienBubeck, @tengyuma
Please join us tomorrow for this interesting presentation if you're interested to learn about self-supervised learning applied to PDEs. #AI4Science #ML4Science #PDEs
📢#AI4Science Talk on 30.11.23 at 15:00 (CET) / 09:00 (ET) on “Self-Supervised Learning with Lie Symmetries for Partial Differential Equations” by @garridoq_. Please join us on Zoom if you're interested! Details: ai4sciencetalks.github.io/projects/ssl4p… #ML4science #AI4Science #Simulations #PDEs
My group is hiring a large cohort of interns for the summer of 2024 to work on the Foundations of Large Language Models! Come help us uncover the new physics of A.I. to improve the LLM building practices! (Pic below from our NeurIPS 2023 paper w. interns) jobs.careers.microsoft.com/global/en/job/…
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NeurIPS Conference @NeurIPSConf
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