Giuliano Antoniol
Giuliano Antoniol is Professor of Software Engineering in the Department of Computer and Software Engineering at Polytechnique Montréal, where he heads the SOCCER laboratory. He has worked in private companies, research institutions and universities. In 2005, he was awarded the Tier I Canada Research Chair in Software Change and Evolution. He has served on the program, organizing and steering committees of numerous international conferences and workshops sponsored by IEEE and ACM. His research interests include software traceability, traceability recovery and maintenance, software evolution, empirical software engineering, research-based software engineering and software testing.
Sebastien Da Veiga
Sébastien Da Veiga is a senior expert in statistics and optimization at Safran, an international high-tech group supplying systems and equipment to the aerospace and defense markets. He obtained his doctorate in statistics from the University of Toulouse in 2007, and his habilitation thesis on interpretable machine learning in 2021. He currently heads a research team working on the use of artificial intelligence for design and simulation. His research interests include computer modeling of experiments, sensitivity analysis, optimization, kernel methods and random forests.
Josée Desharnais
Josée Desharnais is a full professor in the Department of Computer Science and Software Engineering. She has worked on the mathematical foundations of formal verification of interactive Markov systems: logics, bisimulations, metrics. This work earned her two test-of-time awards in 2017 and 2022, from the prestigious international conference on logic: LICS test-of-time award 2017, 2022.
She has some contributions in graph theory on pursuit and search problems.
More recently, she has turned her attention to fundamental research in cybersecurity, focusing in particular on the application of non-interference policies in computer systems to guarantee data confidentiality and integrity. She is also interested in guaranteeing the privacy of intelligent systems and the dependability of their models.
Mélanie Ducoffe
Mélanie Ducoffe has been an industrial researcher at the Airbus Research and Technology Center since 2019, seconded part-time to the DEEL project for the study of robustness in machine learning and its applications to critical systems. Before joining Toulouse, she validated her master's studies with an internship on generative learning with Yoshua Bengio, then completed a PhD in machine learning at CNRS Nice Sophia Antipolis on active learning of deep neural networks. His main current research activities focus on the robustness of neural networks, in particular using formal methods.
Grégory Flandin
With a degree from Supaéro (1997) and a doctorate from Inria (2001), Gregory Flandin began his career at Airbus Defence & Space in earth observation systems engineering. He joined the IRT Saint Exupéry in 2014, where he set up the Artificial Intelligence skills center and then took charge of the Artificial Intelligence for mission-critical systems program.
Sébastien Gerchinovitz
Sébastien Gerchinovitz is a researcher in the DEEL project at IRT Saint Exupéry. He is also a research associate at the Institut de Mathématiques de Toulouse, and a member of the Game Theory and Artificial Intelligence Chair at the Institut d'Intelligence Artificielle et Naturelle de Toulouse (ANITI). Currently on secondment from the Université Paul Sabatier, he obtained his PhD in mathematics from the Ecole Normale Supérieure, Paris. His research topics are statistical learning theory, sequential learning, deep learning and uncertainty quantification for critical systems.
Jean-Michel Loubes
Jean-Michel Loubes is Professor of Applied Mathematics at the Institut de Mathématiques de Toulouse, and holder of the "Fair and Robust Learning" Chair at the Institut d'Intelligence Naturelle et Artificielle de Toulouse (ANITI). After completing his PhD in Toulouse and Leiden, he was a CNRS researcher at the University of Orsay and then at the University of Montpellier from 2001 to 2007. Since 2007, he has been a professor at the University of Toulouse, leading the Statistics and Probability team from 2008 to 2012. He has worked in mathematical statistics on estimation methods and optimal speeds in Machine Learning. His current research focuses on the application of optimal transport theory in Machine Learning, and on issues of fairness and robustness in Artificial Intelligence. Highly involved in links between academia and industry, he was regional manager of the CNRS Agence de Valorisation des Mathématiques from 2012 to 2017, and has been a member of the Scientific Committee of the CNRS Institut des Sciences Mathématiques et de leur Interaction (INSMI) since 2019.
Francois Malgouyres
François Malgouyres holds a PhD in Mathematics applied to image processing from the Ecole Normale Supérieure de Cachan (Paris-Saclay) in 2000, and is now a professor at the Université Paul Sabatier. He is also a member of Deel and ANITI, and works on theoretical and practical aspects of machine learning.
Mario Marchand
Professor in the Computer Science and Software Engineering Department at Université Laval, Mario Marchand has been working in the field of machine learning for over 30 years. His research has focused on performance guarantees for learning algorithms, and on algorithms for optimizing these guarantees. This has led to several works on machine learning applications in various fields, such as drug discovery and antibiotic resistance prediction. More recently, Mario Marchand is interested in learning algorithms that can adapt to different contexts, meta-algorithms for learning how to learn, and the interpretability and explicability of predictions obtained from predictive models learned from data.
Yann Péquignot
Yann Pequignot is a research professional at Université Laval's Institut intelligence et données (IID). A researcher in mathematics and data science, he is in charge of the scientific coordination of the DEEL project.
He is involved in collaborative research with numerous researchers from various universities on the themes of robustness, interpretability and certifiability in artificial intelligence. He also works with various industrial partners to develop knowledge and technologies in artificial intelligence. He has completed postdoctoral training at Université Laval, McGill University in Montreal, UCLA in Los Angeles and the University of Vienna in Austria. He holds a PhD in Mathematics from Université Paris 7.