MIAI Cluster IA - Multidisciplinary Institute in Artificial Intelligence - Université Grenoble Alpes

ISSAI/GAP2026 Grenoble AI for Physical Sciences workshop (GAP)

Thu, 12 Mar 2026 15:22:07 GMT

Description

AISSAI/GAP2026 is the second edition of the Grenoble AI for Physical Sciences (GAP) workshop, following the great success of its inaugural edition in 2024. GAP brings together international experts working at the intersection of artificial intelligence and the physical sciences, with a particular focus on inverse problems, simulation-based inference, data-driven discovery, and foundational models for science. The 2026 edition will continue to highlight cutting-edge advances in theory, algorithms, high-performance computing, and applications across domains such as climate and geosciences, astrophysics, neuroscience, and engineering systems. The format emphasizes pedagogical and forward-looking invited talks that introduce fundamental concepts and survey current challenges and research frontiers. We expect around 200 participants this year. In addition to invited talks, the programme will feature a poster session to showcase ongoing research, encourage exchanges among participants, and stimulate new collaborations across disciplines.

Attendance and registration

The key dates for attending the workshop on-site are:

17-April 2026 : deadline for poster submissions
01-May 2026 : notification of acceptance of poster submissions
15-May 2026 : deadline for all registrations
The event will be streamed at https://videos.univ-grenoble-alpes.fr/live/events/

Keynote speakers

We will have the pleasure of having keynote talks of the following confirmed speakers:

Linus Bleinstein, EPFL
(foundational models for life sciences)

Patrick Galinari, Sorbonne Université
(foundational models for scientific applications)

Samuel Hurault, CNRS, Université Gustave Eiffel
(diffusion models, flow matching, and inverse problems)

Ching-Yao Lai, Stanford University
(physics-informed deep learning for inverse problems)

Fanny Lehmann, ETH Zurich
(foundational models for geophysical sciences)

Jakob Macke, University of Tuebingen
(simulation-based inference)

Julien Mairal, Inria
(inverse problems for imaging)

Laurence Perrault-Levasseur, Université de Montréal
(bayesian methods for astronomy and cosmology)

Nelly Pustelnik, CNRS, ENS Lyon
(model-based neural networks)

Gabrielle Steidl, TU Berlin
(generative modelling)

Poster session

The event will host an engaging poster session that all attendees are invited to join with their own poster. The goal is to make the poster session n key moment to share with other attendees your work related to GAP2026 topics and engage into vibrant discussions. Please submit a short abstract or the poster. The topic needs to be related to AISSAI/GAP2026, with a particular attention to works exploring the synergy between AI and High-Performance Computing (HPC) in the Exascale era. You can submit articles with work-in-progress, work already presented or published at a previous venue (for which you can reuse an already printed poster). A light selection will be done so to ensure that all posters are consistent with the topics of the workshop. Note, however, that there will be no proceedings.

You can submit your poster here

Program

The program is currently being polished and will soon be available.

However, what can be said already is that:

The talks on Wednesday 17-June will be from 9h to 12h30 and 14h to 17h30.

The workshop will propose a standing buffet for the lunch break, during which the contributed posters will be displayed.

The talks on Thursday 18-June will be from 9h to 12h30 and 14h to 17h30.

The workshop will propose a standing buffet for the lunch break, during which the contributed posters will be displayed.

The talks on Friday 19-June will be from 9h to 12h30.

4 th edition of the Deep Learning for Science Day (JDLS)

Thu, 12 Mar 2026 10:49:26 GMT

Presentations in French

What is the Deep Learning for Science Day (JDLS)?

The Deep Learning for Science Day (JDLS), organized by IDRIS and the FIDLE training program, is a national event dedicated to the use of deep learning and artificial intelligence in scientific research.
Open to all (researchers, engineers, PhD candidates, and postdoctoral fellows), JDLS features a series of high-level presentations. Around ten experts will present cutting-edge research in Deep Learning applied to a wide range of fields, including physics, astrophysics, biology, chemistry, law, and the humanities and social sciences.

Designed as a forum for interdisciplinary exchange and networking, JDLS aims to foster dialogue among scientific communities around concrete solutions to advance research through deep learning. A significant part of the program is also dedicated to PhD candidates, who will have the opportunity to present their work during dedicated flash talk and poster sessions.

Registration is not required if you plan to attend the event remotely.

The event will also be streamed live on the FIDLE YouTube channel for those who are unable to attend in person.

See the Programme

Webinar "Cognitive Warfare: Influence and Manipulation in the Age of AI"

Mon, 30 Mar 2026 09:39:29 GMT

Event in French

On March 27, 2026, the “Webinars 2026: The Societal Challenges of AI” series will kick off with a session dedicated to a crucial topic: "Cognitive Warfare: Influence and Manipulation in the Age of AI".

Description

Artificial intelligence is profoundly transforming our relationship with information — from its production and dissemination to its perception and the cognitive mechanisms that underpin it. How can AI both strengthen our defensive capabilities and create new vulnerabilities in the face of influence and manipulation strategies?

This webinar provides insight into the key challenges of cognitive warfare in the age of artificial intelligence.

Speakers

Baptiste Prébot

Baptiste Prébot holds a PhD in Applied Cognitive Science and is an engineering graduate of the École Nationale Supérieure de Cognitique and the University of Bordeaux. He completed a postdoctoral fellowship at Carnegie Mellon University in Pittsburgh.

He currently works as a Human Factors and Human Sciences Expert at the Direction Générale de l’Armement (DGA). Alongside his professional activities, he continues research focused on two main areas: Human-Autonomy Teaming and Cognitive Defense and Security.

Fabrice Jaouen
is a General Officer (Second Section), a graduate of École Spéciale Militaire de Saint-Cyr, and certified by both the French and German War Colleges.

He lectures in business schools on information systems and corporate strategy. He is also coordinator of the Humanities, Society and Artificial Intelligence college within the Association Française pour l’Intelligence Artificielle. His research focuses on the Theory of Inventive Problem Solving (TRIZ) applied to influence and dominance strategies, particularly regarding the use of AI in this context.

Watch the replay of the webinar

MIAI MEETING - 23th of April 2026

Thu, 09 Apr 2026 14:06:26 GMT

From 10:30 am to 2 pm

The next MIAI Meeting, organized in partnership with Medicalps, will take place on April 23th, 2026 at 10:30 AM at Biopolis

Location

Biopolis
5 avenue du Grand Sablon
38700 La Tronche

See the plan

If you are attending in person, registration is mandatory :

Registration is subject to approval

Due to limited capacity at Biopolis, requests to attend in person are subject to approval. Priority will be given to healthcare stakeholders. A confirmation email will be sent to you.

However, participation by video conference remains open to everyone, without restriction.

Registration link

Please note: You can also participate via Zoom, so registration is not required :

Participate via ZOOM

Meeting ID : 939 5628 3743
Secret code : 361424

Automated L2 Speaking Assessment (AL2SA) International Workshop 2026

Mon, 02 Mar 2026 10:12:52 GMT

Recent advances in artificial intelligence, particularly multimodal language models, are opening unprecedented opportunities for automated assessment of speaking skills in second language learning. Yet fundamental questions remain: What exactly should we assess in L2 oral production? How can technology best serve valid, reliable, and ethical assessment practices?

The AL2SA workshop (pron. /ˈæl.tsə/) brings together international experts to address these critical issues. Over two days, we will explore the theoretical foundations, methodological challenges, and practical applications of automated speaking assessment, balancing technological innovation with pedagogical and psychometric rigor.

Key Information

Dates: March 5-6, 2026
Format: Hybrid (on-site + Zoom)
Venue: Salle Jacques Cartier, Maison des Langues et des Cultures, Université Grenoble Alpes
Registration: Free and open to everyone – registration needed
Lunch registration deadline: February 20, 2026

Remote participation

Zoom

Causalworlds 2026: The 3rd International Conference on Quantum, Classical, and Relativistic Causality

Mon, 02 Mar 2026 10:17:58 GMT

Understanding causality is foundational to science and has wide-ranging applications, yet there are several distinct notions of causation. Recently, there have been important developments on the role of causality in quantum physics, relativistic physics and their interplay. These have unearthed a plethora of deep and fascinating questions regarding the nature of causation in physical theories, emergence of space-time structure and how relativistic principles can shape the landscape of quantum information processing. At the same time, causal reasoning is central in classical statistics, and has become a crucial tool in machine learning, with applications ranging from big data to healthcare. The interface between classical and quantum causality has also been crucial for identifying when and how quantum theory can surpass classical models in information processing. Causalworlds aims to bring together researchers from different areas of physics, computer science, mathematics and philosophy working on such questions related to causality, both from fundamental and applied perspectives, to provide a venue for cross-pollination of ideas and techniques across these disciplines and consolidate efforts towards a more unified understanding of causation.

The scope of the conference includes (but is not limited to):

Quantum and classical causal inference and causal models
The role of causal structure in information processing
Indefinite causality and quantum reference frames
Cyclic causality and time symmetry
Causality in quantum field theory and quantum gravity
Experiments in causality and applications

Important dates

Paper submission deadline: Feb 20, 2026
Paper notification: Late March, 2026
Registration deadline: TBC
Conference: June 22-26, 2026

All deadlines are stated with respect to the Anywhere on Earth (AoE) time zone.

Submission Guidelines

We invite submissions of papers for talks and/or posters. Submissions for a talk will automatically be considered for a poster if not accepted for a talk.

Talk: Submission will consist of a 3 page (excluding references) extended abstract, together with a link to the full paper (published or preprint) or a draft of the manuscript if not yet publicly available.

Poster: Submission will consist of a short plaintext abstract. Work in progress is welcome.
Submissions will be considered via EasyChair: https://easychair.org/conferences?conf=cw2026

Invited Speakers

Emily Adlam (Chapman University)
Sougato Bose (University College London)
Fabio Costa (Nordita, Stockholm University)
Eric Gaussier (LIG, Université Grenoble Alpes)
Lee Rozema (University of Vienna)
Mirjam Weilenmann (Inria Saclay, Télécom Paris)
Mark Wilde (Cornell University)

and more to be announced soon.

Graph & Algorithms Days

Mon, 02 Mar 2026 10:18:27 GMT

Each year, national meetings are organized to bring together the GT Graphes community and to disseminate new results: these are the JGA, Graph and Algorithms Days. The next Graph and Algorithms Days (JGA26) will take place in autumn 2026 in Clermont-Ferrand.

AI in Spectral Geometry, perspectives and directions: the Round Table

Mon, 02 Mar 2026 10:16:38 GMT

Workshop theme

Why are all drums round? This can be regarded as one of the earliest motivating questions in spectral geometry. Broadly speaking, spectral geometry explores the interplay between the geometric properties of a space and the spectrum of differential operators defined on it. Its study draws upon deep theories and refined tools from several areas of mathematics, including differential geometry, mathematical physics, partial differential equations, number theory, dynamical systems, and numerical analysis. The field also has numerous real-world applications, ranging from physics and biology to computer science and data analysis.

Although most of the questions about to be addressed during the INI programme on Geometric Spectral Theory and Applications are of a pure mathematics nature, recent years have shown that scientific computing and numerical approximations have become essential tools for gaining insight into deep mathematical phenomena. They not only provide valuable intuitive support for developing new conceptual approaches, but also help in constructing rigorous analytical proofs. A major need within the community is to have accessible tools for computing the spectrum of various differential operators and for exploring its interaction with the underlying geometry, in order to better understand extremal behaviours, singular situations, degenerative phenomena, and so on.

Artificial Intelligence could play a promising role in providing numerical support for computing the spectrum and for searching extremal shapes (or geometries) in spectral optimization problems. Traditionally, identifying optimal shapes for spectral functionals relies on sophisticated numerical techniques. These include computation of the spectrum using finite element methods or fundamental solutions, evaluation of shape and topological derivatives, implementation of level set/phase field/relaxation/homogenisation methods — all combined within tailored optimization algorithms. Currently, only experts in numerical analysis and scientific computing can typically carry out this complex sequence.
However, the spectral geometry community has shown a strong and growing interest in this kind of work.

AI tools offer the potential to introduce a completely new approach. What if we could learn the spectrum — or more broadly, any differential or variational quantity — directly from the geometry? A well-designed learning procedure could bypass the entire chain of traditional numerical approximations of PDEs and replace them with neural networks capable of producing sufficiently accurate estimates. Such computations would not only be significantly faster but could also be integrated into standard optimization algorithms operating in finite-dimensional spaces.

While this approach may not immediately attain the high accuracy of classical methods, it could nonetheless provide spectral geometers with an intuitive, easy-to-use, computationally efficient tool. It may be combined with automated proof algorithms: even though such combination would not directly provide rigorous proofs, this strategy could be efficient for guiding intuition and identifying potential counterexamples that may later be confirmed through rigorous mathematical analysis.
The purpose of the workshop is to bring together specialists from several communities: spectral geometers, numerical analysts, and researchers working in AI geometric data processing and PDEs. The development of AI based spectral analysis requires a deep interaction between them. Our primary objective is to exchange perspectives and gain a deeper understanding of the potential role that artificial intelligence and geometric data processing may play in addressing research questions in spectral geometry, and ultimately to foster the development of new collaborations. We aim to explore how AI might provide new methods, insights, or tools that could help us tackle some of numerical problems related either to the computation of the spectrum or the optimization of the geometry in relationship with spectral functionals.

The workshop will include a series of short presentations followed by an open discussion. The topics of the presentations will cover different aspects from spectral geometry, AI applications in computer graphics, numerical analysis, geometry processing, shape optimization and computer assisted proofs.

Confirmed speakers:

Registrations close on the 22nd March 2026.

Statistical learning Conference 2026

Mon, 02 Mar 2026 10:14:55 GMT

Statlearn is a scientific workshop held every year, which focuses on current and upcoming trends in Statistical Learning. Statlearn is a scientific event of the French Society of Statistics (SFdS) that has been organised since 2010. Conferences and spring schools are organized alternatively every other year.

Registration and Event website

Webinar AI for Good

Mon, 30 Mar 2026 09:41:35 GMT

Event in French

On February 27, 2026, the “Webinars 2026: The Societal Challenges of AI” series will kick off with a session dedicated to a crucial topic: AI for Good.

Description

AI can be a powerful lever for social good in connection with major societal challenges such as health and well-being, climate, and education. The benefits are significant — but so are the risks. Through concrete case studies, we will highlight best practices as well as potential pitfalls.

This webinar will emphasize the importance of appropriate governance and the absolute necessity of keeping humans at the center of AI systems from the very design phase.

Speaker

Agnès Helme-Guizon

Agnès Helme-Guizon is a Full Professor of Marketing at Grenoble IAE-INP and a researcher at the CERAG laboratory within the CRES team (Responsible Behaviors and Societal Issues) at Université Grenoble Alpes. She is also co-lead of the Transformative AI & Social Change (TrAISoc) Chair within the MIAI Cluster.

She co-directs a research stream focused on human collaboration mediated by intelligent systems. Her research, rooted in Social Marketing, aims to identify and implement technological levers that promote healthier lifestyles and improved well-being. She pays particular attention to the unintended or harmful effects of technological tools and critically examines their impact on society.