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 Maillard (PhD Student at École polytechnique), introduce LACONIC, a new 3D layout adapter, pushing generative image models into real 3D. Built on top of existing diffusion […]
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.
Hi! PARIS Summer School 2025Speaker Insight – Aymeric Dieuleveut, École polytechnique 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 […]
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 society. The program provides funding for internal researchers from the Hi! PARIS Cluster 2030 and offers an annual budget with flexibility in allocation between salary, research […]
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.