3IA Chairs Alumni

Topological data analysis  

We are studying the mathematical, statistical, algorithmic and applied aspects of topological data analysis, a fast-growing field with a well-funded theory that is attracting increasing interest in both fundamental research and in industry. Our ambition is to uncover, understand and exploit the topological and geometric structures underlying complex data. 

Mean field multi-agent systems in AI

We study AI systems with a large number of rational agents with mean field interactions. Theoretical questions remain open, specifically when related Nash or Pareto equilibria are not unique, and thus corresponding numerical and learning methods are key issues. Applications include neural networks, power grids, crowd management, cybersecurity, etc. 

Probabilistic machine learning 

Probabilistic machine learning offers a principled framework for quantification of uncertainty across various sciences. The Chair will tackle three major modeling and computational issues: (i) the need to develop practical and scalable tools for accurate quantification of uncertainty, (ii) the lack of interpretability, and (iii) the unsustainable trend in energy consumption. 

Optimal transport for machine learning 

The main objective of this project is to change the way we learn from empirical data using optimal transport. We will first investigate optimal transport for transfer learning with biomedical and astronomical applications. Second, we will adapt the Gromov-Wasserstein distance for structured data and transfer between deep learning models with different architectures. 

AI and mathematics

My research addresses the interactions between research areas in algebra, category theory and geometry, and machine learning. This includes applications of AI to the classification of interesting algebraic and geometric structures, and the interactions between AI and formal verification of proofs in both directions. 

Towards an evolutionary epistemology of ontology learning

I am developing symbolic learning methods based on evolutionary computation to overcome the knowledge acquisition bottleneck in knowledge base construction and enrichment. This project, straddling machine learning and knowledge representation and reasoning, combines symbolic aspects of AI with easily parallelizable computational methods. 

MEL: Modeling and estimating learning 

We are defining new probabilistic models and new estimation methods to understand the deformation of functional connectivity during learning in in vivo experiments. 

AI for Autonomous Surgical Robots

Today's surgical robots are teleoperated mechatronic devices coupled with an imaging system that allow a clinician to perform surgery remotely with improved dexterity, vision, and precision. The future generation of surgical robots will follow the same paradigm as the aeronautics and automotive industries to evolve toward fully autonomous machines. Our ambition is to leverage the latest advances in AI for image processing, context awareness (through sensors and data processing), design, and control to develop a surgical robot that can plan a procedure, interact with clinicians, and perform surgery autonomously under the clinician’s supervision, ultimately providing the best quality of care for all patients.

Improving the Air Travel Experience via Probabilistic Regression with Epistemic Uncertainty Estimation in Adversarial Scenarios

Alix Lhéritier was born in Montevideo, Uruguay, in 1978. He received the Computer Engineer degree and the M.Sc. degree in computer science from the Universidad de la República, Montevideo, Uruguay, in 2004 and 2010, respectively. In 2006, he was a Visiting Research Scholar at the Mathematical Sciences Research Institute, Berkeley, CA and at the Image Processing Laboratory of the University of Minnesota, MN. In 2011, he joined the Algorithms-Biology-Structure (ABS) team at Inria Sophia Antipolis, France as a doctoral student and he received his Ph.D. in computer science from the Université Nice Sophia Antipolis in 2015. He is currently with Amadeus, France, working on machine learning research and applications for the travel industry. His research interests include sequential decision problems, statistical dissimilarity and choice modeling.

Big Knowledge graphs evolution and life cycle

When it comes to deploying big Knowledge Graphs (KG) based information systems in the real world, be it for distributed organisations, small or big companies, some fundamental problems remain unanswered. Our research focuses on solving some of them such as managing the history and evolution of big KG, offering rapid access to KG's data, automatizing the validation of data, and providing generic templating language for more easily exploiting query results.

Trustworthy AI and Explainable Decisions for Business Automation

As we apply Machine Learning to automate decisions in financial services, healthcare and government, there is increasing user need and regulatory demand for transparency and explainability. Our AI research is focused on explainability for decisions that combine ML-based predictions and rule-based business policies.