Intégration des données multi-sources
Intégration des données multi-sources
Anne-Sophie Jannot is an associate professor at the University of Paris Cité and Georges Pompidou European Hospital. Her research deals with methods allowing to identify the optimal trajectories of care for each patient and with knowledge discovery using healthcare data. She is the medical referent for the National Rare Disease Data Bank, a national data warehouse gathering data from all patients followed-up in expert rare disease centers. She is the project leader of DROMOS, the first project to use the National Data Bank for Rare Diseases combined with data from the national health insurance system. This will be the first time that we will be able to analyse the care of rare disease patients in details on a national scale.
Eric Letouzé is a research director at Inserm, co-leader of the "Integrated Cancer Genomics" (ICAGEN) team at the CRCI2NA. Throughout his career, he developed innovative computational strategies to analyze cancer genomics data and unravel the mechanisms of tumorigenesis. The ICAGEN team integrates multi-omics data (including whole genome sequencing and single-cell technologies) in a clinical setting to understand the evolution of cancer cells fostering tumor progression and treatment resistance.
For more than 20 years, Erwan Drézen has worked as a computer engineer in academic (INRIA) and private research (Alcatel, Thomson,...).
In the last decade, he focused on developping algorithms for health data both in genomics and epidemiology. Two years ago, he founded the CUBR company that provides an innovative software solution that exploits at best huge amount of temporal data, including an innovative approach for record linkage of health databases.
Neuroscience et Analyse d’images
Florence Forbes is director of Research at Inria and head of the Statify group. She has been a research scientist at Inria since 1998. She has been working on graphical Markov models, classification methods for spatially localized data and statistical image analysis for more than 20 years. Her publications range from 4 main domains showing a balance at the interface of Statistics and Probability, Machine Learning and Pattern recognition, Signal and Image processing and Biology and medicine. Her current interest consists mainly of model-based clustering methods, supervised (learning) or unsupervised (parameter estimation), statistical model selection and Bayesian techniques to integrate various sources of information and a priori. She had experience with different types of data from domains as diverse as genetics and genomics, computer vision, and planetary science. She has coordinated a number of national projects and participated as co-pi to two European projects. She is also the cofounder and scientific advisor of Pixyl Automatic Neuroimaging Solutions.
Alexandre Gramfort is senior researcher in the Parietal Team at INRIA Saclay Research Center and CEA Neurospin since 2017. He was formerly Assistant Professor at Telecom Paris, Institut Polytechnique de Paris, in the image and signal processing department. His field of expertise is statistical machine learning, signal processing and scientific computing applied primarily to functional brain imaging data (EEG, MEG, fMRI). His work is strongly interdisciplinary at the interface with statistics, computer science, software engineering and neuroscience. He is known for his work on the scikit-learn open source software that he contributed to write since 2010 at Inria, as well as the MNE-Python software that he started while at Harvard in 2011. In 2015, he was awarded a Starting Grant by the European Research Council (ERC).
C. Zimmer is an interdisciplinary scientist active in the fields of biophysics and computational biology. After a PhD in astrophysics in Toulouse (France), and a postdoc in space physics at UCLA (USA), he moved to Institut Pasteur (Paris, France) in 2000, where he has been working on biological image analysis. In 2008, he started the Imaging and Modeling Group (now Unit), an interdisciplinary team of physicists, computer scientists and cell biologists. The lab develops experimental and computational imaging and modeling approaches to address major open questions in cell biology and microbiology. Current research interests include single molecule based super-resolution microscopy, the 3D architecture of chromatin and virus-host interactions. Since 2015, the lab also has an active interest in applying artificial intelligence approaches such as deep learning to the analysis of complex biological and biomedical images. Since mid-2020, C. Zimmer also heads the Computational Biology Department of Institut Pasteur.
IA et éthique
Nicolas Berkouk is a research scientist at EPFL’s Laboratory for Topology and Neuroscience. After graduating in pure mathematics from École Polytechnique and Imperial College, he obtained his Ph.D. in 2020 from INRIA - École polytechnique, in which he applied modern techniques of algebraic topology to machine learning. At EPFL, he is now collaborating with the Swiss start-up L2F in order to further our understanding of neural networks behaviors through the lens of algebraic topology. In parallel, he collaborates with sociologist Pierre François (Sc. Po. Paris) and philosopher Laurence Barry (Chaire PARI) to study the recently born domain of Explainable AI.
Jean-Michel Loubes is Professor of Statistics at the Mathematical Institute of Toulouse, University of Toulouse since 2007. Since 2019 he holds the Chair of Fair and Robust Learning at the Artificial and Natural Intelligence Toulouse’s Institute (ANITI). Since 2020, he has been elected Deputy President of the University Toulouse 3 in Charge of Innovation for Industry. He started his career as a CNRS Researcher at University of Paris Saclay and worked on asymptotic results under sparse constraints in mathematical statistics. Then his research interests turned towards developing new relevant methodological tools from probability theory and statistics to solve applications from several fields including data science, medical applications, anthropology, industrial applications and modelling of complex models. Recently, he obtained, together with his collaborators, important results in optimal transport theory and its applications to statistics and machine learning. In particular, he is interested in obtaining new methods to impose constraints to machine learning algorithms based on Monge-Kantorovich distance, which enable to reduce algorithmic biases and improve fairness of automatic decisions in Artificial Intelligence.
Océane Fiant has a PhD in Philosophy of Science, History of Science and Technology (Nantes Université, Centre François Viète). Her thesis is entitled "Algorithms before artificial intelligence: issues, practices and contexts of medical decision-making's automation by means of two case studies." Océane is currently a post-doctoral fellow at Université de technologie de Compiègne (Costech) in the MaLO project (Machine Learning Decision Support Systems in Oncology: Epistemological, Ethical and Legal Issues), funded by Institut National du Cancer. Her research focuses on the epistemological and ethical issues associated with the development of artificial intelligence in medicine.