Section: Overall Objectives

MOdel for Data Analysis and Learning

Modal is a team focused on statistical methodology for data analysis (clustering, visualization) and learning (classification, density estimation). In this context, the core of the team's work is to design meaningful generative models for prominent complex data (heterogeneous structured data), which are still almost ignored in the literature. Application domains are numerous (credit scoring, marketing,...), but modal favors applications related to biology and medicine. Members of the team are already experienced in these directions with complementary skills.

The team scientific objectives are split into two main methodological directions: Generative model design and data visualization through such models. In each case, several means of dissemination are considered towards academic and/or industrial communities: Publications in international journals (in statistics or biostatistics), workshops to raise or identify emerging topics, and publicly available specific softwares relying on the proposed new methodologies.