I am mainly interested in the use of stochastic processes to model and infer biological processes, particularly ecological processes.
Recently I focused mainly on movement ecology application and the development of statistical methods to extract knowledge from the study of animals, but also fishing vessels movement.
Segmentation. Developping model to infer underlying activities based on the observation of the movement. This research relies on Hidden Marko Model for movement and change point detection method.
Stochastic differential equations. SDE is an appealing probabilistic object to represent movement because of the continuous time and continuous space formulation of the model and a nice, but probably questionable, Markov property.
Computationnal statistics. Hidden variables models are a flexible class of models as soon as you are able to estimate their parameters, this often relies on simulation approaches and I enjoy proposing algorithm to achieve the estimation of such models.
Spatial Zero inflated data. Abundance indices are derived from capture data where the probability of no capture is higher than expected with classical distribution. Data who present some excess of zeros are named zero inflated data. I’m interested in proposing statistical approaches to handle zero inflated data in a spatial context to build robust abundance indices.
Most of my applications are related to biology and ecology, I have been specially interested in two main applications
Fisheries I’m interested in Fisheries science and I have develop collaborations with colleagues from Ifremer, UMR ESE and UMR Marbec to develop methods to analyze fishing vessel movement and build abundance indices in stock assesment context.
Animal movement ecology Developping methods for movement ecology has been one of my main focus in the last years. I am very interested at developping collaborations with ecologist to improve the existing models.