AI4Science

SCAI

Centre de conférences internationales - Sorbonne Université

The initiative brings together several stakeholders from the SCAI/Sorbonne University ecosystem who develop AI methods for physics, climate science, chemistry, materials science, biology, and mathematics, alongside machine learning experts. 9:00 - Welcome coffee9:30 - Opening remarks from the institutions (Sorbonne University, CNRS, Inria)9:45 - Keynote 1 – <a href="https://aero.engin.umich.edu/people/duraisamy-karthik/" title="https://aero.engin.umich.edu/people/duraisamy-karthik/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Karthik Duraisamy</a> (University of Michigan) "AI-Augmented Discovery Engines: Progress, Opportunities and Emerging Ecosystems"Scientific progress is entering a new phase: one where frontier reasoning models, domain foundation models, classical computational science, scientific instruments and human experts function as a tightly coupled agentic system. The real bar is not benchmark performance, but scientific utility: accelerating validated insight, compressing discovery and design cycles, and narrowing the sim-to-real gap in complex multiscale physics. I will describe recent advancements at the University of Michigan, highlighting (i) expressive, domain-specific foundation models for scientific prediction and inference, and (ii) agentic AI infrastructures that orchestrate tools, data, and HPC workflows for hypothesis generation, verification/ validation, and design optimization, while keeping humans in the loop to set objectives, constraints, and scientific judgment. I will also discuss the ambitious University of Michigan–Los Alamos National Laboratory superpartnership and place these efforts in the broader U.S. AI4Sciencelandscape, spanning public-private partnerships, national-scale infrastructure, and community-building initiatives. I will close by arguing that the next leap will come less from any single breakthrough model and more from the co-design of models, methods, instruments, and institutions.10:30 - AI4Science at SCAI/Sorbonne Université- AI4Chemistry by <a href="https://www.sorbonne-universite.fr/portraits/jean-philip-piquemal" title="https://www.sorbonne-universite.fr/portraits/jean-philip-piquemal" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jean-Philip Piquemal</a> (LCT)- AI4Materials by <a href="https://impmc.sorbonne-universite.fr/fr/equipes/physique_des_systemes_simples_en_conditions_extremes_physix/membres/permanents/marco_saitta.html" title="https://impmc.sorbonne-universite.fr/fr/equipes/physique_des_systemes_simples_en_conditions_extremes_physix/membres/permanents/marco_saitta.html" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Marco Saitta</a> (LPENS) "Materials for Energy from AI-driven approaches at ENS and in France"This presentation highlights recent AI-driven approaches to atomistic simulations in computational materials science and their impact on the study of energy materials, including batteries and supercapacitors. Selected results from our research group at ENS are discussed within the broader context of the French AI–Materials–Energy research ecosystem.- AI4Biology by <a href="https://www.lcqb.upmc.fr/users/alessandra-carbone" title="https://www.lcqb.upmc.fr/users/alessandra-carbone" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Alessandra Carbone </a>(LCQB) "Decoding protein function at scale: AI from structures to systems"Deep learning has profoundly transformed biology by revealing the structure of the protein universe, with AlphaFold and related models providing atomic-level reconstructions at unprecedented scale. Yet structure is only the beginning. The next grand challenge is functional understanding: determining what proteins do, how they interact, and how they collectively give rise to biological processes. Recent advances now make it possible to address this gap ab initio. By learning directly from protein sequences, new models can reconstruct proteome-wide interactomes, predict interaction interfaces, and classify large families of homologous proteins by function. This shift enables a systems-level interpretation of genomes, where unknown proteins can be placed into complexes, pathways, and regulatory networks, opening the door to scalable and mechanistic functional annotation. This talk will present these emerging approaches, our contributions to sequence-based functional inference, and how they are being deployed within ambitious French initiatives such as ATLASea for marine biodiversity and PostGenAI for human health. Together, these efforts illustrate how AI is reshaping our ability to interpret life at scale, and how the next phase of AI-driven biology will move from structures to systems. 11:30 Round table 1: AI4Science as a pathway to innovation. Moderation by <a href="https://pages.isir.upmc.fr/gallinari/" title="https://pages.isir.upmc.fr/gallinari/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Patrick Gallinari</a> (ISIR)Panelists:<ul><li><a href="https://www.lkb.fr/laboratoire/membres/sylvain-gigan-2/" title="https://www.lkb.fr/laboratoire/membres/sylvain-gigan-2/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Sylvain Gigan</a> (Lighton)</li><li><a href="https://neilashton.co.uk/" title="https://neilashton.co.uk/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Neil Ashton</a> (Nvidia),</li><li><a href="https://www.sorbonne-universite.fr/portraits/jean-philip-piquemal" title="https://www.sorbonne-universite.fr/portraits/jean-philip-piquemal" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jean-Philip Piquemal</a> (Qubit),</li><li><a href="https://alexduvalinho.github.io/" title="https://alexduvalinho.github.io/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Alexandre Duval</a> (Entalpic),</li><li><a href="https://www.linkedin.com/in/romuald-elie-b9bb817b/?originalSubdomain=fr" title="https://www.linkedin.com/in/romuald-elie-b9bb817b/?originalSubdomain=fr" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Romuad Elie</a> (Google DeepMind)</li><li>others TBD.</li></ul>12:30–13:45 Buffet lunch13:45 - Keynote 2 – <a href="https://jthaler.net/" title="https://jthaler.net/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jesse Thaler</a> (MIT, IHES): " Centaur Science: Adventures in AI+Physics"The mythical centaur (half human, half horse) has become a metaphor for human-AI collaboration. In this talk, I explore what centaur science looks like at the intersection of artificial intelligence and fundamental physics. I share adventures from both directions of this exchange: teaching machines to "think like a physicist" by incorporating physics principles into machine learning frameworks, and teaching physicists to "think like a machine" to maximize discovery opportunities in both experimental and theoretical physics.14:30 - AI4Science At SCAI/Sorbonne Université- AI4Climate by <a href="https://www.colorado.edu/faculty/claire-monteleoni/" title="https://www.colorado.edu/faculty/claire-monteleoni/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Claire Monteleoni</a> (INRIA-ARCHES)- AI4Astrophysics by <a href="https://www.guilhem-lavaux.eu/" title="https://www.guilhem-lavaux.eu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Guilhem Lavaux</a> (IAP) "Cosmic scales: integrating Bayesian inference and deep learning to reveal new physics"Astronomy confronts the paradox of studying objects we can never touch, while ever‑improving, high‑fidelity data strain our simulation and inference pipelines. In this talk I will showcase several AI breakthroughs—Bayesian‑deep hybrids, neural surrogates, and probabilistic models—and the fresh discoveries they have enabled in exoplanet characterization, galaxy‑scale physics, and cosmological modeling across multiple observational regimes and theoretical frameworks.- Physics-aware AI by <a href="https://pages.isir.upmc.fr/gallinari/" title="https://pages.isir.upmc.fr/gallinari/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Patrick Gallinari</a> (ISIR)15:30 - Table ronde 2: AI for Science, Science for AI Moderation by Paola Cinnella (D’Alembert, SCAI)Panelists:<ul><li><a href="https://perso.lpsm.paris/~biau/" title="https://perso.lpsm.paris/~biau/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Gérard Biau</a> (SCAI-MEGAVOLT)</li><li><a href="https://fadili.users.greyc.fr/" title="https://fadili.users.greyc.fr/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jalal Fadili</a> (AISSAI)</li><li><a href="https://syrte.obspm.fr/spip/science/histoire/membres-de-l-equipe/article/matthieu-husson" title="https://syrte.obspm.fr/spip/science/histoire/membres-de-l-equipe/article/matthieu-husson" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Matthieu Husson</a> (Observatoire de Paris)</li><li><a href="https://aero.engin.umich.edu/people/duraisamy-karthik/" title="https://aero.engin.umich.edu/people/duraisamy-karthik/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Karthik Duraisamy</a> (University of Michigan)</li><li><a href="https://jthaler.net/" title="https://jthaler.net/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jesse Thaler</a> (MIT)</li><li><a href="https://indico.cern.ch/event/1514154/contributions/6670821/attachments/3137285/5567248/COMETA_Saclay.pdf" title="https://indico.cern.ch/event/1514154/contributions/6670821/attachments/3137285/5567248/COMETA_Saclay.pdf" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Anja Butter</a> (LPNHE, CERN),</li><li>others TBD</li></ul>16:30 - Summary, recommendations, next steps and closing.17:00 End of program (approx.) Scientific committee:<ul><li>Gérard Biau</li><li>Paola Cinnella</li><li>Bruno Desprès</li><li>Patrick Gallinari</li><li>Bertrand Laforge</li><li>G. Levaux</li><li>Claire Monteleoni</li><li>Jean-Pierre Piquemal</li><li>Marco Saitta</li><li>Julien Salomon</li><li>Martino Trassinelli</li><li>Martin Weigt</li></ul>

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