Highlight Research

IP Paris – Hi! PARIS Computer Vision Workshop

The IP Paris – Hi! PARIS 2026 Computer Vision Workshop is a collaborative event bringing together researchers, students, and professionals passionate about advancing the field of computer vision. Hosted at Télécom Paris, the workshop will explore the latest research breakthroughs, foster cross-disciplinary discussions, and connect participants around innovative ideas shaping the future of AI and visual perception. Whether you […]

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Training humans with machines that understand movement

What if your coach was always available, never tired, and capable of instantly analyzing your posture to help you move better, whether you’re recovering from an injury, learning a new dance move, or practicing the violin? For Gül Varol, chair in the 2025 cohort, this isn’t science fiction. It’s the ambition behind her research project: […]

Call of project Highlight

Call for Scientific Events and AI Conferences 2026

DIM AI4IDF – Open until February 16, 2026 (1 p.m.) The DIM AI4IDF is launching its Call for Scientific and AI Event Proposals 2026, aimed at supporting initiatives related to artificial intelligence in the Île-de-France region.This call is open to academic, industrial, and associative teams wishing to organize a scientific event that contributes to the […]

Highlight Research Society
Meet Up on AI & Future of Work

Hi! PARIS Meet Up! on AI & The Future of Work

As part of the Hi! PARIS Initiatives, this Meet Up on “AI & the Future of Work” will bring researchers, industry leaders, and practitioners together at Station F. The goal: to reflect on how AI is reshaping jobs, skills, and workplace dynamics, and to explore what this transformation means for companies, policymakers, and society.

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LACONIC: A 3D layout adapter for controllable image creation

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 […]

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Rethinking Uncertainty in Machine Learning

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.