Chairs | AI for Smart and Secure Territories

• 4D urban modeling
• Predict & exploit user behaviors and preferences
• Anticipate & manage possible disasters 

• Local & global optimization of systems with active users 
• Accounting for diversity, heterogeneity, uncertainty, dynamics, preferences, etc. 

3D modeling of large-scale environments for the smart territory 

We are exploring the generation of 3D models from raw measurement data such as 3D point clouds. We explored a progressive shape reconstruction method, a supervised learning approach to detect sharp features in 3D point clouds and a novel clustering method for variational shape reconstruction method based on quadric error metrics. We are currently exploring a novel approach with capability to embed a differentiable version of 3D Voronoi diagrams - via a deep learning architecture - into a generative deep network.

Privacy-preserving machine learning 

Machine learning has become popular due to cloud computing technology and the increasing number of datasets. Outsourcing computations poses a risk to data privacy. Therefore, our goal is to explore privacy-preserving variants of machine learning techniques while leveraging novel cryptographic methods.

Deep law for tech (DL4T) 

The DL4T team is building the legal framework for deep technologies. Starting from the observation that law is often perceived as a force of resistance to innovation, we want to position our research upstream of technology, to support the emergence of technical standards and to promote a convergence between law and AI.

Territorial Security through environmental risks management

This project deals with risk assessments related to environmental extreme events. Analyses and predictions of floods, summer heatwaves, and storms are significant questions facing statisticians and risk assessors. Such environmental risks are the result of a long chain of casualties, involving several aleas, often correlated, with complex spatio-temporal dependent structures among extremes.

Our contributions in the prevention and management of environmental risks, will be twofold: 1/ Proposing novel and realistic definitions of risks indicators in environmental contexts. 2/ Studying in-depth their statistical inference, i.e. specifying more accurately the associated uncertainties.

In this project, the skills required to handle the modeling of these uncertainties are stochastic processes and random fields, spatio-temporal models, multivariate extreme theory, as well as practical expertise on spatial and environmental data gathered from firms in 3IA Côte d’Azur.

Internet of Learning Thing, a machine learning approach to future IoT networks

In this chair, we develop cooperative forms of decision making, that can be implemented on distributed IoT devices, and not relying on the assumption that all data is centralized in the cloud. IoT devices can learn to coordinate with each other in their usage of the wireless spectrum, energy, and other resources while dealing with arbitrary noise uncertainties in their observation data.
Cooperative machine learning will bring a profound evolution in IoT system design, both at the level of radio access, as well as in the manner services will be orchestrated and how resources will be allocated.

Data driven traffic management

This project aims at contributing to the transition to intelligent mobility management practices through an efficient use of available resources and information, fostering data collection and provision. We focus on improving traffic flow on road networks by using advanced mathematical models and statistical techniques leveraging the information recovered by real data. We are committed in creating a network of local stakeholders sharing knowledge and expertise.

Distributed dark fiber optic sensing for smart cities monitoring

Optical fiber, in addition to being a means of transmitting information, is also a material that is very sensitive to environmental variations. When a laser light pulse travels through an optical fiber, it interacts with tiny impurities in the material and optical backscattering occurs. The round-trip time of the light provides the locations of interactions and allows us to infer a backscattering profile along the fiber. Processing this response provides estimates of the local variations in temperature, deformation or acoustic pressure along the fiber. This technique, called Distributed Fiber Optic Sensing (DFOS), is currently experiencing growing interest.

The goal of our project is to develop a breakthrough framework for smart cities monitoring based on DFOS over existing dark fibers, and Artificial Intelligence.