At this year’s Hi! PARIS Summer School, Solenne Gaucher (École polytechnique) shed light on the growing challenge of fairness in AI. As algorithms trained on biased data shape decisions at scale, she reminded us that fairness is neither only a mathematical problem nor only an ethical one. Instead, it sits at the intersection of both, and demands attention from scientists, policymakers, and society alike.
From October 19 to 25, Hi! PARIS researchers will be in Honolulu, Hawaii, for the International Conference on Computer Vision (ICCV 2025), one of the most important gatherings worldwide in the field. 10 papers from Hi! PARIS affiliated teams have been accepted this year, a recognition of the quality of our work across partner institutions.
We are proud to announce that Anna Korba, Assistant Professor in Statistics at CREST-GENES, Professor at ENSAE Paris, and Hi! PARIS Affiliate, has been awarded a European Research Council (ERC) Starting Grant for her project OptInfinite.
Optimal Transport for Machine Learning is in the spotlight of the Hi! PARIS Reading groups in October-December 2025, a scientific networking action gathering affiliates and corporate donors around important topics of the moment!
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.
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