Artificial intelligence has mastered language, vision, and even strategy games, but can it master mathematics? Amaury Hayat’s DESCARTES project explores one of the boldest frontiers in science: teaching machines not just to calculate, but to reason.
While generative AI tools continue to impress, their inner workings remain largely mysterious. Hi! PARIS Fellow Alain Oliviero Durmus is tackling this challenge head-on with his project TODO – Toward Enhanced Generative Models. By applying tools from stochastic optimal control, he’s building a stronger mathematical foundation for diffusion and flow models, aiming to make them more robust, interpretable, and ready for complex real-world applications.
What happens when artificial intelligence pushes the boundaries of image creation from flat, 2D visuals into fully controllable 3D scenes? In their work, Maks Ovsjanikov (Professor at École polytechnique) and Léopold […]
Every measurement, whether in physics, statistics, or machine learning, comes with a cost. From Heisenberg’s uncertainty principle to the limits of data prediction, Professor Xiao-Li Meng reminds us that knowledge itself is bounded by trade-offs. Precision and uncertainty are not opposites, they are partners in the same dance. In science, as in life, there is no free lunch.
This year, 36 papers from Hi! PARIS affiliated researchers have been accepted at NeurIPS 2025, one of the world’s most prestigious conferences in artificial intelligence and machine learning., highlighting the strength and breadth of our research across partner institutions.
A strong showing that reflects our continued commitment to advancing the frontiers of AI for science, business, and society.
As machine learning systems become embedded in critical decisions, from finance to infrastructure, the need for trustworthy, interpretable predictions has never been greater. Aymeric Dieuleveut, Professor of Statistics and Machine Learning at École polytechnique and scientific co-director of the Hi! PARIS Center, believes the key lies not in the models themselves, but in how we communicate their uncertainty.
The Hi!ckathon is a flagship AI and Data Science challenge organized by Hi! PARIS, the interdisciplinary center for data science and artificial intelligence.
The call is open only to Hi! PARIS has launched the 2026 Internal Fellowship call to support long-term research and teaching in AI and Data Analytics for Science, business and […]
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.
The Hi! PARIS AI Seminar Cycle is a monthly series showcasing leading research in Artificial Intelligence and Data Science. Held on the first Wednesday of each month, it brings together top scholars, students, and partners to explore AI’s scientific, business, and societal impact across key themes such as foundation models, trustworthy AI, and AI for science and engineering.