At this year’s Hi! PARIS Summer School, Anna Korba (ENSAE Paris) took a fresh look at Langevin diffusions, an old idea from physics that’s quietly becoming central to generative modeling. As machine learning and mathematics increasingly overlap, she invites us to pay closer attention to what’s happening under the hood of today’s most talked-about models.
AI is no longer a support tool in biology, it’s becoming a scientific partner.”
At the Hi! PARIS Summer School 2025, Jean-Philippe Vert (Bioptimus) explored how AI is impacti biomedical research. From protein folding breakthroughs like AlphaFold to in silico simulations of disease, Vert made the case for a new era where biology’s complexity meets AI’s learning power. The next frontier? Models that understand life across molecules, cells, and entire organisms.
Ioannis Stefanou, new Hi! PARIS Chair-holder and Professor at ENSTA, is combining AI, mechanics, and physics to advance the energy transition. His work explores how technologies like geothermal energy and hydrogen storage can be modeled through physics-informed AI, creating smarter, more sustainable systems for the future.
Luiz Chamon, new Hi! PARIS Chair-holder at École polytechnique, focuses on making AI trustworthy by design.
His work rethinks how intelligent systems learn, embedding fairness, safety, and robustness from the start to ensure AI serves society responsibly.
How is AI changing the way we think about financial markets? In this talk, Charles-Albert Lehalle draws on experience across academia and industry to explore what’s really shifting, from the use of alternative data to the role of retail investors.
Launched during the French President’s state visit to the UK, the Entente CordIAle Paris-Saclay – Oxford-Cambridge AI Initiative unites Institut Polytechnique de Paris, HEC Paris, Université Paris-Saclay, Oxford, and Cambridge to advance AI for science, business, and society.
At its core, Hi! PARIS plays a key role in driving this €70M partnership, supporting joint research, PhD programs, mobility initiatives, and industry collaboration to foster responsible and sovereign AI.
From July 13 to 19, Hi! PARIS researchers will take part in the International Conference on Machine Learning (ICML 2025), one of the world’s leading conferences in artificial intelligence, held this year in Vancouver, Canada. A total of 27 research papers from Hi! PARIS affiliated teams have been accepted following ICML’s highly selective peer-review process, recognizing the excellence and innovation of our researchers across partner institutions. This strong presence reflects Hi! PARIS’s scientific leadership and deep engagement in frontier AI research. The accepted papers span a wide array of domains, including machine learning theory, optimization, generative models, privacy, and multi-agent systems, and demonstrate the center’s interdisciplinary strength. By contributing to ICML 2025’s core sessions and workshops, Hi! PARIS reaffirms its commitment to advancing AI research for science, business, and society at the highest international level. Congratulations to our researchers! Here is a list of papers accepted at ICML 2025 that include at least one author affiliated with Hi! PARIS: Title Hi! PARIS Authors All Authors Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO* Albert Bifet, Jesse Read Ayman Chaouki, Jesse Read, Albert Bifet To Each Metric Its Decoding Thomas Bonald, Matthieu Labeau Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald Scaling Laws for Forgetting Marco Cuturi Louis Béthune, David Grangier, Dan Busbridge, Eleonora Gualdoni, Marco Cuturi, Pierre Ablin Shielded Diffusion Marco Cuturi Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye, Marco Cuturi Misspecification in Simulation-based Inference Marco Cuturi Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn Jacobsen, Marco Cuturi TabICL Gael Varoquaux Jingang QU, David Holzmüller, Gael Varoquaux, Marine Le Morvan Byzantine Robust Gossip Aymeric Dieuleveut Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx Scaffold with Stochastic Gradients Aymeric Dieuleveut, Alain Oliviero Durmus, Eric Moulines Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Eric Moulines Compressed and Distributed Least-Squares Aymeric Dieuleveut Constantin Philippenko, Aymeric Dieuleveut Discrete Markov Probabilistic Models Alain Oliviero Durmus Le Tuyet Nhi PHAM, Dario Shariatian, Antonio Ocello, Giovanni Conforti, Alain Oliviero Durmus Prediction-Aware Learning Alain Oliviero Durmus, Eric Moulines Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael Jordan, Alain Oliviero Durmus Mixture-Based Framework for Diffusion Models Alain Oliviero Durmus, Eric Moulines Yazid Janati el idrissi, Badr MOUFAD, Mehdi Qassime, Alain Oliviero Durmus, Eric Moulines, Jimmy Olsson Differential Privacy for MCMC Alain Oliviero Durmus Andrea Bertazzi, Tim Johnston, Gareth Roberts, Alain Oliviero Durmus Asymmetric Actor-Critic Algorithms Damien Ernst Gaspard Lambrechts, Damien Ernst, Aditya Mahajan Score-Based Generative Models in W2 Marta Gentiloni Silveri Marta Gentiloni Silveri, Antonio Ocello Sliced-Wasserstein Distance Analysis Anna Korba Christophe Vauthier, Anna Korba, Quentin Mérigot Density Ratio Estimation Anna Korba Hanlin Yu, Arto Klami, Aapo Hyvarinen, Anna Korba, Lemir Omar Chehab Wasserstein Gradient Flows Anna Korba Clément Bonet, Christophe Vauthier, Anna Korba Learning of Continuous Markov Semigroups Karim Lounici Vladimir Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil GNN with GMM Augmentation Johannes Lutzeyer, Michalis Vazirgiannis Yassine Abbahaddou, Fragkiskos Malliaros, Johannes Lutzeyer, Amine Aboussalah, Michalis Vazirgiannis TRPO in Mean Field Games Eric Moulines Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Lauriere, Eric Moulines Conditional Coverage with Conformity Scores Eric Moulines Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb, Maxim Panov, Eric Moulines Efficient On-Device Learning Van-tam Nguyen, Enzo Tartaglione Le-Trung Nguyen, Aël Quélennec, Van-Tam Nguyen, Enzo Tartaglione Pareto-Optimality in One-Max-Search Vianney Perchet Ziyad Benomar, Lorenzo Croissant, Vianney Perchet, Spyros Angelopoulos Last Iterate Convergence for Uncoupled Learning Vianney Perchet Côme Fiegel, Pierre Menard, Tadashi Kozuno, Michal Valko, Vianney Perchet Quantifying Treatment Effects Erwan Scornet Ahmed Boughdiri, Julie Josse, Erwan Scornet Prediction via Shapley Value Regression Michalis Vazirgiannis Amr Alkhatib, Roman Bresson, Henrik Boström, Michalis Vazirgiannis
We are proud to introduce the newest cohort of Hi! PARIS Chairs, part of the Hi! PARIS Cluster 2030 initiative. Designed to foster long-term excellence in AI and data analytics, this program supports innovative research across science, business, and society. Hosted within leading institutions, Institut Polytechnique de Paris, HEC Paris, Inria, and CNRS, the 2025 chairs were selected through a highly competitive open call. Our new cohort includes 19 exceptional researchers, spanning Standard (Starting & Advanced) and Synergy Fellowships. They are hosted across Hi! PARIS partner instituions and bring diverse, forward-thinking AI applications: Starting Fellowships Gül Varol (ENPC) – Training Machines to Train Humans in Motion Enzo Tartaglione (Télécom Paris) – Frugal AI: Grounding Information Flow Vicky Kalogeiton (Ecole polytechnique) – Scalable Model-Data Co-evolution Stephan Alaniz (Télécom Paris) – Explaining & Removing Social Biases in Text-to-Image AI Amaury Hayat (ENPC) – Efficient Symbolic Mathematics Systems Advanced Fellowships Antonin Bergeaud (HEC Paris) – AI Adoption: Economic & Social Implications Loïc Landrieu (ENPC) – Universal Geospatial Representations Ioana Manolescu (Inria) – AI Data Dialogs for the Press Alain Oliviero-Durmus (Ecole polytechnique) – Enhanced Generative Models Etienne Ollion (CNRS) – Textual Politics in Traditional Media Fabian Suchanek (Télécom Paris) – Lossy Language Models Synergy Fellowships Florence D’Alché-Buc (Télécom Paris), Rémi Flamary (Ecole polytechnique), Charlotte Laclau (Télécom Paris) & Karim Lounici (Ecole polytechnique) – Advancing efficient, reliable and science-informed Learning for non-euclidean data with Application to molecule and biological network Structures Patrick Loiseau (Inria), Vianney Perchet (ENSAE Paris), Yuki Tamura (Ecole polytechnique) & Pablo Winant (Ecole polytechnique) – Design, Incentivization, Optimization and Reinforcement Learning of Multi-Layered Market. This fellowship cohort reflects the depth and diversity of the Hi! PARIS research ecosystem. From geospatial modeling and symbolic reasoning to AI ethics, media analysis, and market dynamics, the selected projects embody the center’s commitment to advancing interdisciplinary and socially impactful research. Through its Starting, Advanced, and Synergy Fellowship tracks, Hi! PARIS is proud to support both emerging talent and established leaders, fostering collaboration across institutions and disciplines to drive the future of AI for science, business, and society.
Exceptional Seminar with Prof. Eric Xing at École polytechnique Hi! PARIS is honored to welcome Professor Eric Xing, President of the Mohamed bin Zayed University of Artificial Intelligence and Professor at Carnegie Mellon University, for an exceptional scientific seminar on July 8, 2025, at École Polytechnique. As one of the leading figures in AI and machine learning, Prof. Xing will present his latest work on general and purposeful reasoning systems beyond traditional language models. This event is part of our commitment to fostering high-level scientific exchange and advancing cutting-edge AI research. Abstract The success of large language models (LLMs) like OpenAI’s GPTs has amazed the world with their extraordinary performance in tasks such as standardized tests, advanced mathematical reasoning, encyclopedic knowledge retrieval, and human-like language generation. However, these models fall short when it comes to embodied reasoning, physical and social understanding, and real-world strategic planning. In this seminar, Prof. Xing will introduce “PAN”, a novel architecture designed for general and purposeful reasoning in complex, real-world environments. PAN combines a new World Model for steerable simulations of potential outcomes and a dynamic Agent Model capable of planning, learning, and adapting to achieve goals across diverse contexts. He will contrast this approach with current architectures, addressing common misconceptions around agent and world models, and discuss choices of data, representations, and training strategies. The talk will also feature theoretical insights and a preview of an upcoming model release. Prof. Xing will conclude with a broader reflection on concepts such as agency and artificial general intelligence (AGI), informed by a renewed philosophical lens. About the Speaker Professor Eric Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence and a Professor of Computer Science at Carnegie Mellon University. His research spans machine learning, large-scale distributed computing, and statistical modeling, with recent focus on foundational models in biology and AI. He has received multiple prestigious awards, including the NSF CAREER Award, the Sloan Fellowship, and best paper awards at leading conferences like ACL, ISMB, NeurIPS, and OSDI. He is a Fellow of the AAAI, ACM, ASA, IEEE, and IMS, and has served on editorial boards for top journals in the field.
We are proud to announce that Arnak Dalalyan, director of CREST, Hi! PARIS Fellow and Professor of statistics at ENSAE Paris, has been awarded a European Research Council (ERC) Advanced Grant for his project SAGMOS.