Research in Artificial Intelligence

The activities of MIAI Grenoble Alpes are structured around two main themes future AI systems and AI for human beings and the environment and organised to support the development of seven main axes. In terms of scientific activities, those axes are then divided into specific programmes, within which the chairs are embedded.
Next Stage AI
Grenoble is one of the most active places in France and Europe in core AI areas - machine learning, perception, speech communication, computer vision and embedded AI. Indeed, MIAI contributors have published, in the last three years, over 30 papers at ICML, KDD and NIPS, over 60 papers in major computer vision conferences as CVPR, ECCV and ICCV and have ranked first in several international challenges (as SemEval or ImagNet). Three European AI Fellows further contribute to research in symbolic AI. In addition, the Grenoble area includes major academic actors in hardware design, HW/SW acceleration and distributed computing.

Theme details: Next Stage AI

Axis 1. Machine learning and reasoning
  • 1.1. Machine learning models
  • 1.2. Statistics and optimization
  • 1.3. Fair and evolvable AI
Axis 2. Embedded and distributed AI, and hardware architecture for AI
  • 2.1. Neuro-processing units
  • 2.2. Distributed intelligence
Axis 3. Perception & interaction
  • 3.1. Robotics
  • 3.2. Natural language and speech processing
  • 3.3. Computer vision 

AI for human beings & the environment
Grenoble is also at the forefront in medicine, earth and climate research. In computer assisted medical interventions, several devices developed in Grenoble have led to surgical premieres (e.g. the navigational surgery for prostate biopsies commercially developed by Koelis, a spin-off of Univ. Grenoble Alpes, that has been used by over 250 000 patients in more than 30 countries). By using massive processing of seismic and geodetic data, researchers in Grenoble have revealed that the solid earth is evolving at all time scales, an observation that is challenging the traditional views of its dynamics. By harnessing massive large-scale species distribution data and high resolution climatic and remote sensing data, researchers in Grenoble have revealed how climate and land use change triggers unexpected responses of biodiversity. MIAI will pursue and extend these research lines in several directions.


Theme details: AI for Human Beings & the Environnement

Axis 4. AI & society
  • 4.1. AI regulation
  • 4.2. Integration of AI into society
Axis 5. Health
  • 5.1. Real-life 4P medecine
  • 5.2. Multiomics
  • 5.3. Computer-assisted medical intelligence
Axis 6. Environment & energy
  • 6.1. AI solutions for natural disasters
  • 6.2. Optimising energy management
Axis 7. Industry 4.0
  • 7.1. Human-centric manufacturing
  • 7.2. Predictive quality