Research Interests:

ANR SMILES (338 K€); 2018-2022
Principal Investigaor.

Large-scale data analysis is an inherently multidisciplinary area and is becoming of broader interest for today's society. The ANR project SMILES is a collaborative fundamental research project that aims at introducing an unsupervised statistical modeling framework and scaled inference algorithms for transforming large-scale data into knowledge. It considers the large-scale context as a whole, with its main issues related to inference from a big volume of data of very high dimension and underlying complex hidden structures. The key tenet of SMILES is to introduce large-scale latent data models for unsupervised data classification and large-scale regression-based sparse (non)parametric models for data representation. The knowledge extraction will namely consist in automatically retrieving hidden structures, summarizing prototypes, groups, sparse representations. We consider different data settings, including functional data, multimodal bioacoustical data, and biological data. Learn more

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AStERiCs (300 K€); 2018-2020
Principal Investigaor.

AStERiCs Est un projet de recherche fondamentale financé dans le cadre du dispositif RIN (Réseaux d'Intérêts Normands)-Recherche dont l'objectif structurel est de fédérer la recherche scientifique en Normandie dans le domaine de la science statistique des données, en s'appuyant sur une démarche scientifique pluridisciplinaire impliquant modélisation mathématique, inférence, représentation et classification de données issues d'environnements complexes, hétérogènes, dynamiques et incertains. AStERiCs vise à élaborer un cadre scientifique et technique, complet, pour traiter, analyser, exploiter et valoriser des données massives, complexes, hétérogènes, dynamiques et peu ou non-annotées. Le but est de transformer des données en connaissances sous forme de représentations précises des informations liées aux données, de catégorisations pertinentes de telles informations, jusqu’à la valorisation de celles-ci en révélant/restaurant le modèle générateur des données. Le projet AStERiCs traite ainsi le problème de la grande échelle, sous tous ses aspects de modélisation et d'inférence. Plus précisément, les axes de recherche traitent des grands thèmes suivants : Statistique, Apprentissage, Analyse de données, Classification, Optimisation, Traitement du signal, Grande dimension. Learn more

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S4D (~19 K€); 2017-..
Principal Investigaor.

The Research Summer School on Statistics for Data Science (S4D) is a multidisciplinary international summer school at the interface of several disciplines: statistics, computer science (machine learning), optimization and signal processing, with a focus on probabilistic/statistical formulations of recent methodologies dedicated to the problem of learning from large-scale complex data. It involves, during a week, for an audience of PhD students, engineers, postdocs and scientists, several international pioneering scientists in modern statistics, machine learning and artificial intelligence, to present their expertise on both theoretical and technical aspects of models and algorithms to address data science problems with a focus on large-scale/high-dimensional scenarios. The objectives of the talks are to: - present the basic developments of models for phd students and practitioners to understand the basics of the domain; - highlight for students, scientists and practitioners the major scientific issues necessary for the development of such models and on the related theoretical and computational problems; - present and analyze the latest theoretical advances in modeling and practical considerations regarding statistical learning, classification, representation, of large-scale raw data, in various application area Learn more

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