Collège de France - Sélection

Samantha BessonDroit international des institutionsCollège de FranceAnnée 2024-2025Conférence - Hilary Charlesworth : La Cour internationale de Justice et ses critiquesHilary CharlesworthJuge auprès de la Cour internationale de Justice de La HayeHilary Charlesworth est invitée par l'assemblée du Collège de France sur proposition de la Pr Samantha Besson.La conférence est en anglais.RésuméCette conférence examinera certaines des critiques générales adressées à la Cour internationale de Justice et s'efforcera d'y répondre. Il s'agit notamment de la critique des chercheurs réalistes, qui soutiennent que la Cour est essentiellement un organe politique, mais sans aucun pouvoir ; de la critique des chercheurs du Tiers Monde (TWAIL), qui soulignent les origines coloniales de la Cour et les éléments de colonialisme qui traversent ses jugements ; et de la critique des chercheurs féministes, qui s'intéressent à la sous-représentation des femmes à la Cour, et à l'effet qu'elle a sur la jurisprudence de la Cour.La juge Charlesworth est une éminente juriste qui a apporté une contribution exceptionnelle à l'étude et à la pratique du droit international. Elle est juge à la Cour internationale de Justice (CIJ) depuis son élection en novembre 2021. Avant de rejoindre la CIJ, Hilary Charlesworth était Laureate Professor à la faculté de droit de l'université de Melbourne et Distinguished Professor à l'université nationale australienne (ANU). Elle est diplômée de l'université de Melbourne et de la faculté de droit de Harvard, où elle a obtenu son doctorat. Les recherches et publications primées de Hilary Charlesworth couvrent divers domaines, notamment la structure du système juridique international, la consolidation de la paix, les droits de l'Homme et le droit humanitaire, ainsi que des travaux novateurs sur le genre et le droit international et sur la démocratie internationale.

Grand événement - À la recherche d'un Avenir Commun DurablePour une épistémologie de l'enquête sur les socio-agrosystèmes préindustrielsLe cas de l'agriculture irriguée de la Lombardie médiévaleCollège de FranceAnnée 2024-2025Projet sous la direction de : Patrick Boucheron, chaire Histoire des pouvoirs en Europe occidentale, XIIIe-XVIe siècle et François-Xavier Fauvelle, chaire Histoire et archéologie des mondes africains.Patrick BoucheronProfesseur du Collège de FranceLouise GentilPostdoctorante, Collège de FranceSalomé TissolongJournaliste indépendanteRésuméDepuis vingt ans, les « humanités environnementales » constituent un nouvel horizon de la recherche pour l'ensemble des sciences sociales. Cet essor est intimement lié aux préoccupations contemporaines grandissantes que nourrit la destruction par l'espèce humaine de son propre milieu de vie. Toutefois, l'historicisation des rapports des sociétés à leur environnement apparaît encore insuffisante, alors même qu'elle permet à la fois de tenir à distance de supposées évidences qui faussent les débats actuels, et de faire émerger des concepts utiles à la relecture des relations entre les humains et les composantes de leur milieu de vie.En effet, si les sociétés agraires préindustrielles fonctionnaient selon des équilibres que l'on peut – selon nos catégories contemporaines – qualifier de « durables », elles n'ont pourtant été épargnées ni par les logiques d'exploitation intensive ni par les volontés de maximisation de la productivité des hommes et des terres, comme le montre l'agriculture irriguée de Lombardie entre le XIIIe et le XVIe siècle.

Jean-François JoannyMatière molle et biophysiqueCollège de FranceAnnée 2024-2025Conférence - Mark Bowick - Order, Geometry and Defects: Facets of OrderMark BowickDeputy Director, Kavli Institute for Theoretical Physics, University of California Santa BarbaraMark Bowick est invité par l'assemblée du Collège de France sur proposition du professeur Jean-François Joanny, chaire Matière molle et biophysique.RésuméSharp structures can occur as minimizers of very regular problems. This means symmetry can lead us badly astray and the resultant symmetry breaking may lead to highly counter-intuitive structures. I will illustrate in a discrete example and then some continuous examples. The main soft matter example will be the emergence of faceted shapes as the ground states of liquid-crystalline vesicles and the novel ground states of crystalline order on a 2-sphere.Mark BowickMark Bowick was born in New Zealand, obtained his B.Sc.(Hons) from the University of Canterbury (New Zealand) and a Ph.D. in theoretical particle physics from Caltech in 1983. This was followed by a Research Associate position in the Particle Theory Group at Yale, winning the First prize award in the 1986 Gravity Research Foundation Essay Competition. This was followed by a postdoctoral position in the Center for Theoretical Physics at MIT. He joined the faculty of the Physics Department at Syracuse University in 1987, holding an Outstanding Junior Investigator award from the Department of Energy from 1987-1993 and becoming Full Professor of Physics in 1998. His research career has been split between theoretical high-energy physics and condensed matter (principally soft matter). He was elected a Fellow of the American Physical Society (Division of Condensed Matter Physics) in 2004 and Fellow of the American Association for the Advancement of Science in 2022. He received the Chancellor's Citation for Exceptional Academic Achievement in 2006 (Syracuse University) and the William Wasserstrom Prize for Excellence in Graduate Teaching and Advising in 2009 (Syracuse University). From 2011-2016 he was Director of the Soft Matter Program at Syracuse University.Since 2016 Bowick has been Deputy Director of the Kavli Institute for Theoretical Physics (Santa Barbara) and Distinguished Visiting Professor of Physics at the University of California Santa Barbara.

Samantha BessonDroit international des institutionsCollège de FranceAnnée 2024-2025Conférence - Liam Murphy - Legal Practice and the Responsibility of IndividualsLiam MurphyProfesseur de philosophie du droit, New York University (NYU) Law SchoolLiam Murphy est invité par l'assemblée du Collège de France, sur proposition de la Pr Samantha Besson. Dans le cadre de la convention signée entre le Collège de France et la New York University.La conférence est en anglais.RésuméSome legal practices, such as the private law of obligations and property, are justified by the good that general compliance with their rules bring about. It cannot be said, however, that each particular act of compliance by individuals itself contributes to that good outcome. And yet there is clearly an ethical tie between individuals and the rules of the practices. Leaving aside cases where the law simply protects independent moral rights, the same points can be made about compliance with law generally. This lecture explores the question of how we should understand the ethical tie between individuals and legal practices that are justified in terms of the social good produced by general compliance. An imperfect duty of impartial beneficence will play a central role in the account.iam Murphy works in legal, moral, and political philosophy and the application of these inquiries to law, legal institutions, and legal theory. Subjects of his publications range from abstract questions of moral philosophy (for example, "Nonlegislative Justification" in Jeff McMahan et al., Principles and Persons: The Legacy of Derek Parfit, 2021) to concrete issues of legal and economic policy (for example, The Myth of Ownership: Taxes and Justice, 2002, co-authored with Thomas Nagel). A central theme in all Murphy's work is that legal, moral, and political theory cannot be pursued independently of one another; they are, in fact, different dimensions of a single subject. This theme is evident in his book What Makes Law (2014), which locates the traditional philosophical issue of the grounds of law (the factors that determine the content of the law in force) within broader issues of political theory. Much of Murphy's recent work has been in the field of private law theory, though he has also recently returned to tax policy, writing a new paper with Thomas Nagel on wealth taxation. Going forward, Murphy is working on a book project that concerns the connections and differences among the justifications of practices (including legal practices) and the moral requirements that apply to individuals, collectives of individuals, and states. Murphy has been awarded fellowships at Columbia's Society of Fellows in the Humanities, Harvard's Society of Fellows, and the National Humanities Center. He was vice dean of NYU School of Law from 2007 to 2010.

Dominique CharpinCollège de FranceAnnée 2024-2025Civilisation mésopotamienneAu pays des deux fleuves : environnement et sociétés en Mésopotamie ancienneConférence - Hervé Reculeau : Faire parler l'argile : à la recherche des paysages fantômes de MésopotamieHervé ReculeauAssociate Professor of Assyriology, Institute for the Study of Ancient Cultures, ChicagoRésuméEn géologie comme en archéologie ou en épigraphie, il faut partir du présent pour reconstruire le passé. Pour cette première conférence, nous nous attacherons aux données et méthodes permettant de reconstruire les environnements anciens de Mésopotamie, via la géologie, la paléoclimatologie, l'archéologie de site et des paysages, ainsi que l'étude historique des documents cunéiformes. Cette introduction méthodologique servira de point de départ aux exposés suivants en montrant, à travers plusieurs études de cas, comment ces paysages fantômes se manifestent dans leur variété de formes et d'états de conservation, ce qu'il est possible d'en dire et ce que l'on ignorera sans doute toujours.

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Laure Zanna : Reshaping climate modelling with AILaure ZannaKeller Professor of Applied Mathematics, NYU CourantRésuméWhile AI has been disrupting conventional weather forecasting, we are only beginning to witness the impact of AI on long-term climate simulations. The fidelity and reliability of climate models has been limited by computing capabilities. These limitations lead to inaccurate representations of key processes such as convection, cloud, or mixing or restrict the ensemble size of climate predictions. Therefore, these issues are a significant hurdle in enhancing climate simulations and their predictions.Here, I will discuss a new generation of climate models with AI representations of unresolved ocean physics, learned from high-fidelity simulations, and their impact on reducing biases in climate simulations. The simulations are performed with operational ocean model components. I will further demonstrate the potential of AI to accelerate climate predictions and increase their reliability through the generation of fully AI-driven emulators, which can reproduce decades of climate model output in seconds with high accuracy.Laure ZannaProfessor Zanna is a climate physicist in the Department of Mathematics at the Courant Institute, and the Center for Data Science, NYU. She holds the Joseph B. Keller and Herbert B. Keller Professorship in Applied Mathematics. Her research focuses on understanding, simulating and predicting the role of the ocean in climate on local and global scales. She combines theory, numerical simulations, statistics, and machine learning to tackle a wide range of problems in fluid dynamics and climate, including turbulence, multiscale modeling, ocean heat and carbon uptake, and sea level rise. Since 2020, she is leading M²LInES, an international collaboration sponsored by Schmidt Sciences dedicated to improving climate models using scientific machine learning. In 2020, Prof Zanna received the Nicholas P. Fofonoff Award from the American Meteorological Society "for exceptional creativity in the development and application of new concepts in ocean and climate dynamics", and was the 2022 WHOI Geophysical Fluid Dynamics principal lecturer.

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Marc Bocquet : Artificial intelligence for geophysical data assimilationMarc BocquetCEREA, ENPC, EdF R&D, Institut Polytechnique de ParisRésuméData assimilation is the set of key mathematical methods used to optimally combine observations and numerical model outputs. Data assimilation (DA) is critical to adjust the initial condition of meteorological forecasts, to estimate model parameters, and produce accurate re-analysis datasets. It has been at the heart of all operational weather forecasts for the past 50 years. Very recently, artificial intelligence (AI) and in particular deep learning, has begun being used as a tool to improve classical DA, to be combined with DA algorithmic schemes, or even to offer a substitute for DA.I will give an overview of the recent achievements and promising routes offered by AI into DA.For instance, ML can be leveraged in the regularisation of ensemble-based DA, in the solvers of variational DA methods, for generating or augmenting ensembles in DA, for building surrogates of the tangent linear and adjoint meteorological models to be used within DA, to learn a model error correction within a weak-constraint 4D-Var framework, or, ultimately, as a replacement for the DA analysis. I will also present an example where AI unveils new DA methods that were overlooked so far by the research community.Marc BocquetMarc Bocquet holds a Ph.D. from École Polytechnique and has an Habilitation delivered by Sorbonne University. He was a postdoctoral fellow at the University of Warwick and then at the University of Oxford. He is currently deputy director of CEREA, a laboratory of École nationale des ponts et chaussées and EDF R&D, and a professor at École nationale des ponts et chaussées, Institut Polytechnique de Paris. He works on data assimilation, inverse problems and statistical learning applied to the geosciences. He develops mathematical methods to better estimate the state of the atmosphere, of the ocean and the climate, as well as their constituents, using massive observations and complex models. He has published 115 papers and two books. He is associate editor of several peer-reviewed journals in the geosciences, and a Distinguished Research Fellow of the world's most renowned weather forecasting centre, the European Centre for Medium-Range Weather Forecasts (ECMWF).

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Remi Lam : Learning global weather forecasting from dataRemi LamMassachusetts Institute of Technology, Staff Research Scientist, Google DeepMindRésuméThis presentation will cover some of the recent advances in weather forecasting, learning directly from data using machine learning techniques.It will discuss some of the limitations and pitfalls of training ML models for scientific applications, and will highlight new research opportunities.Rémi LamRémi Lam is a Staff Research Scientist at Google DeepMind working on making weather forecasting faster and more accurate.His research leverages machine learning techniques such as adversarial neural networks, graph neural networks and diffusion models to design tools for precipitation nowcasting (DGMR) and global medium range weather prediction (GraphCast, GenCast).

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Claire Monteleoni : Confronting climate change with generative and self-supervised machine learningClaire MonteleoniResearch Director, INRIA Paris & Professor, University of Colorado BoulderRésuméRésuméThe stunning recent advances in AI content generation rely on cutting-edge, generative deep learning algorithms and architectures trained on massive amounts of text, image, and video data. With different training data, these algorithms and architectures can also be used to confront climate change. As opposed to text and video, the relevant training data includes weather and climate data from observations, reanalyses, and even physical simulations. As in many massive data applications, creating "labeled data" for supervised machine learning is often costly, time-consuming, or even impossible. Fortuitously, in very large-scale data domains, "self-supervised" machine learning methods are now actually outperforming supervised learning methods. In this lecture, I will survey our lab's work developing generative and self-supervised machine learning approaches for applications addressing climate change, including downscaling and temporal interpolation of spatiotemporal data and generating probabilistic weather predictions.Claire MonteleoniClaire Monteleoni is a Choose France Chair in AI and a Research Director at INRIA Paris, a Professor in the Department of Computer Science at the University of Colorado Boulder (on leave), and the founding Editor in Chief of Environmental Data Science, a Cambridge University Press journal launched in December 2020. Her research on machine learning for the study of climate change helped launch the interdisciplinary field of Climate Informatics. She co-founded the International Conference on Climate Informatics, which will hold its 14th annual event in 2025. She gave an invited tutorial: Climate Change: Challenges for Machine Learning, at NeurIPS 2014. She currently serves on the U.S. National Science Foundation's Advisory Committee for Environmental Research and Education, and as Tutorials co-Chair for the International Conference on Machine Learning (ICML) 2024 and 2025.

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Thomas Dubos : Hamiltonian insights and the challenge of unresolved processes in geophysical modelsThomas DubosProfesseur, École PolytechniqueRésuméMathematical and numerical models of the atmosphere and ocean rely on various assumptions, approximations, and simplifications. Over the past decade, significant progress has been made in elucidating their structure and interconnections, particularly for the resolved and reversible fluid components. This advancement has largely been driven by Hamiltonian approaches, encompassing both Hamilton's principle of least action and the associated symplectic structure. Moreover, these insights have influenced the development of numerical methods in production-ready models.This progress shifts attention toward the unresolved and irreversible processes, where mathematical and theoretical foundations remain scarce—and may continue to be so. I will challenge the notion that partial differential equations are all that is needed, and highlight areas where theoretical progress seems possible. Hopefully, this perspective can shed light on the respective roles of physics-based and data-driven components in comprehensive models.Thomas DubosThomas Dubos studied mathematics and physics at the École Normale Supérieure (Paris) and obtained his Ph.D. in 2002 at the Laboratoire de Météorologie Dynamique (LMD), focusing on transport through two-dimensional turbulence. As Assistant Professor and then Professor at LMD/École Polytechnique, his research focused on geophysical turbulence and hydrodynamics. More recently, he has used Hamiltonian methods to uncover the structure and connections of existing geophysical models, to derive new models, and to develop numerical methods with desirable physical properties. This fundamental work has contributed to the development of the LMDZ, the atmospheric general circulation model developed at LMD, which is part of the Earth System Model at the Institut Pierre-Simon Laplace (IPSL).