ENSAI Hosts the Mascot-Num International Conference
From April 1 to 3, ENSAI will host the 2026 edition of the annual Mascot-Num International Conference, a leading event dedicated to statistics and the quantification of uncertainties. This year’s presentations will focus on the fields of the environment and health.
Nearly 90 participants from academia and public and private organizations are expected to attend.
Mascot-Num: Statistics and Uncertainty Quantification Applied to the Environment and Health
At a time when public policy decisions must account for increasing levels of uncertainty—related to climate change, public health, and risk management—Mascot-Num brings together researchers, experts, and doctoral students to better understand and anticipate these complex phenomena and help inform decision-making.
The first day is dedicated to doctoral students, featuring presentations on cutting-edge topics: sensitivity analysis, uncertainty quantification in simulation, design and modeling of computational experiments, data-driven modeling methods, and more.
The second and third days are devoted to presentations by international researchers focusing on statistics and the quantification of uncertainties for environmental and health applications, through sessions such as: quantification of uncertainty in climate reanalyses, insights from human genetic clustering, and the interpretability of electricity demand forecasting models…
Talks by invited speakers
Rémi Bardenet (CNRS, Université de Lille): Tutorial – What is the Gaussian process of point processes?
Margaux Brégère (EDF Lab Paris Saclay, Sorbonne Université): Explainability of electricity demand forecasting models: a Shapley value approach for positive component decomposition
Lucas Drumetz (IMT Atlantique): Spatialized Bayesian inference and uncertainty quantification on constrained co-domains with Gaussian Processes: application to the simplex
Pierre Gloaguen (Université Bretagne Sud): Bayesian Modelling of Abundance Data in Ecology Using Joint Species Distribution Models
Javier González-Delgado (ENSAI): Inference after human genetic clustering
Sylvain Le Corff (Sorbonne Université): On Forgetting and Stability of Score-based Generative models
Apolline Louvet (INRAE Avignon): Percolation, weeds and Bayesian inference – Assessing seed bank influence on plant metapopulation dynamics
Natalie Maus (MIT)
Pierre Tandeo (IMT Atlantique): Quantifying uncertainty in climate reanalyses
Learn more about research at ENSAI and CREST