L'atelier « AI for Science » s'inscrit dans le cadre d'une collaboration universitaire plus large réunissant l'université d'Oxford, l'université de Cambridge, l'université Paris-Saclay (via l'Institut DataIA) et l'Institut polytechnique de Paris (via Hi! Paris).

Cet événement de deux jours est consacré à l'étude de la manière dont l'intelligence artificielle transforme la recherche scientifique dans toutes les disciplines.

Conçu comme un forum interactif et collaboratif, l'atelier propose une série d'exposés de 40 minutes présentés par des chercheurs, ainsi que des occasions pour les participants de présenter leurs propres travaux. Ce format encourage la participation active, la discussion et l'émergence de nouvelles collaborations au sein d'un environnement hautement interdisciplinaire.

Session de posters

Les sessions de posters constituent un élément central de l'atelier ; elles offrent aux participants l'occasion de présenter leurs posters scientifiques et d'échanger directement avec leurs pairs et des chercheurs plus avancés.

Un espace dédié sera mis à disposition pour l'affichage des posters, garantissant une visibilité continue tout au long de l'événement. Les posters seront présentés pendant les pauses déjeuner, favorisant ainsi les discussions informelles et le réseautage.

Les participants souhaitant présenter un poster pourront l'indiquer lors de leur inscription. En cas de nombre élevé de soumissions, un comité scientifique procédera à une sélection. Tous les présentateurs sont tenus d'imprimer et d'apporter leurs posters sur place.
 

Programme

Le programme détaillé de l'atelier « L'IA au service de la science » sera publié prochainement.

Intervenants confirmés :

• Tom Nichols -

  (Oxford).

  Thomas Nichols is the Professor of Neuroimaging Statistics at the University of Oxford Big Data Institute. He is a statistician with a solitary focus on modelling and inference Bio : methods for brain imaging research. He has both industrial and academic experience and a diverse training including computer science, cognitive neuroscience and statistics. He was the Director Modelling and Genetics at GlaxoSmithKline's Clinical Imaging Centre, London, and has been at Oxford since 2017. The focus of his work is developing modelling and inference methods for brain image data, specifically Magnetic Resonance Imaging (MRI) and functional MRI.  His current work includes methodology for population scale neuroimaging data, longitudinal studies and neuroimaging genetics.

• Bartek Papiez -

  (Oxford).

  Bartlomiej (Bartek) Papiez is Group Lead for Medical Image Understanding and Machine Learning at the Big Data Institute, Associate Prof at Nuffield Department of Population Health,  University of Oxford, and an Official Fellow in AI/ML at Reuben College. His research spans both the theoretical foundations of AI/ML, including data fusion, explainability, and computational fairness, and applied machine learning for longitudinal disease monitoring using imaging, patient records, and natural language processing. He also works on identifying therapeutic targets through the integration of imaging and genetic data. His current application areas include cardiovascular disease, arthritis and rheumatic diseases, and cancer.

• Philip Stier - (Oxford).

  Philip Stier is a Professor of Atmospheric Physics in the Department of Physics, where he leads the Climate Processes Group, and a Fellow of Reuben College. He also serves as Director of Intelligent Earth—Oxford’s UKRI AI Centre for Doctoral Training in AI for the Environment—and is a member of the steering group of the Oxford Climate Research Network. His research focuses on physical climate processes in the context of anthropogenic perturbations to the Earth system, which are the underlying cause of climate change and air pollution. His main areas of interest include cloud and aerosol physics, their interactions, and their role in the climate system. Within the Climate Processes Group, he and his team combine complex numerical models with Earth observations and AI/machine learning to advance theoretical understanding and improve the predictability of the climate system.

• Antonio Silveti-Falls - (Institut DataIA - Université Paris-Saclay)

  Antonio (Tony) Silveti-Falls is an associate professor (maître de conférences) at CentraleSupélec in the south of Paris, where he is a member of the Centre pour la Vision Numérique laboratory and the INRIA team OPIS. After receiving his PhD in mathematics from Université de Caen Normandie in 2021, where he was supervised by Jalal Fadili and Gabriel Peyré, he completed a postdoc at Toulouse School of Economics with Jérôme Bolte and Edouard Pauwels. His research continues to focus on {nonsmooth, stochastic, noneuclidean} optimization, especially conditional gradient methods (Frank-Wolfe) and conservative calculus (path differentiable functions) applied to deep learning. His work on the generalized conditional gradient method won the best paper award at SPARS 2019. 

• Stergios Christodoulidis - (Institut DataIA - Université Paris-Saclay)

• Pietro Gori - (Hi! PARIS, Institut Polytechnique de Paris)

  Pietro Gori is Professeur (PhD,HDR) in Artificial Intelligence and Medical Imaging at Télécom Paris (IPParis) in the IMAGES group. He did his PhD with Inria at the ARAMIS Lab in Paris and then a post-doc at Neurospin (CEA). Previous to that, he obtained a MSc in Mathematical Modelling and Computation from the DTU in Copenhagen and a MSc in Biomedical Engineering from the University of Padova. He participated to the development of the open source software suite deformetrica for statistical shape analysis and of the software platform Clinica for clinical neuroimaging studies. His research interests lie primarily in the fields of machine learning, AI, representation learning, medical imaging and computational anatomy. He has more than 60 publications in international peer-reviewed journals or conferences, 2 patents and had/has the pleasure to work with more than 25 PhD students and post-docs. He is also the co-founder of the Start-Up Replico.

• Maxime Di Folco - (...)

  Maxime Di Folco is an Assistant Professor in Artificial Intelligence for Medical Imaging within the IMAGES group. He completed his PhD at INSA Lyon within the CREATIS laboratory, followed by a postdoctoral fellowship under the supervision of Julia Schnabel at Helmholtz Munich and the Technical University of Munich.  Prior to that, he obtained a Master of Engineering in Numerical Sciences from CPE Lyon and an MSc in Image Development and 3D Technologies from Université de Lyon. His research focuses on representation learning and multimodal approaches for trustworthy decision-making in medical imaging and healthcare AI.

• Maria Vakalopoulou - (Institut DataIA - Université Paris-Saclay)

• Sibo Cheng - (Hi! PARIS, Institut Polytechnique de Paris)

  Sibo Cheng is currently a Junior Professor at CEREA, ENPC, Institut Polytechnique de Paris in France and an Honorary Research Fellow at the Department of Computing at Imperial College London. His work focuses on machine learning for dynamical systems, reduced-order models, and inverse modeling (parameter calibration and data assimilation) for environmental science and physics, with a wide range of applications including wildfire and air pollution. He completed his Ph.D. at LISN, University Paris-Saclay, France, in 2020. From 2020 to 2024, he was a Research Associate at the Data Science Institute of Imperial College London. His current research is supported by ANR, PEPR, and Hi!Paris.

• Cengiz Öztireli - (University of Cambridge)

• Pietro Liò - (University of Cambridge)

  Pietro Liò is a Professor of Computational Biology at the University of Cambridge and a member of Clare Hall. His research lies at the intersection of machine learning, network science, and biology, with a strong focus on understanding complex biological systems such as gene regulation, disease mechanisms, and aging. He has been a pioneer in applying graph-based methods, including graph neural networks, to biomedical data, enabling the integration of heterogeneous information across molecular, cellular, and clinical scales. His work spans topics such as systems biology, precision medicine, and the modeling of chronic diseases, often combining mathematical rigor with data-driven approaches.

• Cyril Furtlehner - (Inria / UPS — LISN)

  Cyril Furtlehner is an Inria research scientist based at the Inria-Saclay research center. With an original  background in theoretical physics,his present research lies at the intersection between statistical physics and machine learning, with a focus on probabilistic inference, stochastic processes, energy-based models and more recently physics informed machine learning. He has applied these ideas to problems such as traffic modeling and forecasting, the theoretical analysis of learning algorithms leading in particular to efficient restricted Boltzmann machine training methods. More broadly, his work explores how methods from statistical physics can be used to understand and design machine-learning systems.

• Sean Holden - (University of Cambridge)

• Florence Tupin - (IPP — Télécom Paris)

  Florence Tupin is currently a Professor of image and signal processing at Telecom Paris, where she is also head of the Image, Data, and Signal Department. Her research focuses on image processing and analysis, particularly for remote sensing and synthetic aperture radar imaging applications, and Earth observation.
  She has been a member of several international and national technical conference committees since 2003. She  has been co-chair of the GRETSI Technical Program Committee since 2024 and IEEE GRSS (Geoscience and Remote Sensing Society) Distinguished Lecturer since 2025. She has received several awards, including the IEEE GRSS Transactions Prize Paper Award in 2016, the IEEE GRSS Symposium Prize Paper Award in 2022 and the IEEE GRSS Letters Prize Paper Award in 2026 for her work on speckle filtering.

Programme previsionnel :

Jeudi 21 :

• 9h30 - 10h : Accueil café

• 10h - 10h30 : Ouverture - Institut DataIA

• Session one : AI for Health — Brain, Representation & Clinical Imaging

• 10h30 - 11h15 : Conférence - Tom Nichols (Oxford — Big Data Institute)

• 11h15 - 12h :Conférence - Stergios Christodoulidis (UPS — Institut DataIA)

• 12h - 12h15 : Pause.

• 12h15-13h : Conférence - Cengiz Öztireli (University of Cambridge)

  --

• 13h - 14h15 : Déjeuner (sessions posters)

  --

• Session two : AI for Health — Imaging & Disease Modelling

• 14h15 - 15h : Conférence - Bartek Papiez (Oxford — Big Data Institute)

• 15h - 15h45 : Conférence - Maria Vakalopoulou (UPS — Institut DataIA)

• 15h45 - 16h : Pause.

• 16h-16h45 : Conférence - Pietro Gori - (Hi! PARIS, IPP — Télécom Paris)

• 16h45- 17h30 : Conférence - Pietro Liò (University of Cambridge)

• 17h30 : Cocktail (sessions posters)

   

Vendredi 22 :

• 9h30h - 10h : Accueil café.

  Session three : AI for Earth & Physical Sciences

• 10h - 10h45 : Conférence - Philip Stier (Oxford — Atmospheric Physics)

• 10h45 - 11h30 : Conférence - Sibo Cheng (IPP, Hi! PARIS — ENPC)

• 11h30-11h45 : Pause.

• 11h45-12h30 : Conférence - Cyril Furtlehner (Inria / UPS — LISN)

  --

• 12h30 - 13h45 : Déjeuner (sessions posters)

  --

  Session four : AI Methods & Foundations

• 13h45 - 14h30 : Conférence - Antonio Silveti-Falls (UPS — Institut DataIA)

• 14h30 - 15h15 : Conférence - Sean Holden (University of Cambridge)

• 15h15 - 15h30 : Pause.

• 15h30 - 16h15 : Conférence - Florence Tupin (IPP — Télécom Paris)

• 16h15 - 16h45 :  Prise de parole finale.

Informations pratiques :

• Date : Jeudi 21 et vendredi 22 mai.
• Heure : 9h30 h à 17h (à confirmer).

• Lieux : Bâtiment Turing, Inria Saclay, Palaiseau - Amphithéâtre Sophie Germain

  1 rue Honoré d'Estienne d'Orves - 91120 Palaiseau

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