A monthly seminar series connecting students, PhD candidates and researchers with leading voices in artificial intelligence.
The AI Seminar Cycle is organized by Hi! PARIS, in collaboration with the ELLIS program on Theory, Algorithms, and Computations of Modern Learning Systems and the Paris ELLIS Unit.
This partnership aims to broaden the visibility of the seminar among the European AI research community.
The series offers the Hi! PARIS and broader AI research community the opportunity to discover emerging research topics, engage directly with leading researchers and explore new perspectives across the field.
Most sessions are held online, making the seminar accessible to a broad academic community in France and across Europe.
2026–2027 Seminar Program
Talk title and abstract coming soon.
Wednesday, October 7 (11AM – 12PM) – Online (Register)
Talk title and abstract coming soon.
Research areas include probabilistic machine learning, generative AI and uncertainty quantification.
Wednesday, November 4 (11AM – 12PM) – Online (Register)
Talk title and abstract coming soon.
Research areas include human-centered AI, trustworthy AI and explainable AI.
Wednesday, April 14 (11AM – 12PM) – Online (Register)
More sessions will be announced throughout the academic year.
About the Seminar Cycle
The AI Seminar Cycle is designed primarily for students, PhD candidates and researchers, while remaining open to the broader AI research community.
Each session offers an opportunity to:
discover research topics beyond one’s immediate field;
hear directly from researchers working on current and emerging AI challenges;
exchange ideas through questions and discussion;
strengthen connections across the European AI research community.
Sessions generally last one hour, including the presentation and discussion with the audience.
Previous Editions
2025-2026
Discover the speakers, topics and sessions from the 2025–2026 AI Seminar Cycle.
→ The 2025–2026 program and past sessions:
Xiao-Li Meng, Harvard University | No Free Lunch: “From a Simultaneous (Machine) Learning Impossibility to Heisenberg Uncertainty Principle” | October 2, 2025
Elisa Ricci, University of Trento, Hi! PARIS SAB Member | No labels, no training: Leveraging Language for Detecting Anomalous Events in Videos | November 5, 2025
Spyros Gidaris, Valeo AI | Latent Representations for Better Generative Image Modeling | December 10, 2025
Oriane Siméoni, Meta | Research Session on Vision | February 4, 2026
Patrick Perez, Kyutai | Small talk is harder than it sounds | March 4, 2026
Francis Bach, Inria | A spectral framework for closed-form relative density estimation | June 3, 2026
Previous editions of the seminar will continue to be archived here as the series develops.