I aim at understanding, analysing and fitting dependent random models, such as time series, random fields, random processes or point processes. My mathematical background led me to introduce new models, as well as to fit and test them. For this, I was the advisor for 20 PhD or Habilitation theses. As demonstrated by my two most cited monographs, I am strongly involved into probabilistic dependence structures such as strong mixing or long range dependence. To relax usual mixing conditions, I introduced weak dependence with Sana Louhichi. My early introduction of wavelets in statistics was also important. In addition to my recent elementary book on times series modeling, specific additional issues such as incomplete data, integer valued models and high dimensional data are needed. Additional features of real data are also their non stationarity, their possibly high dimension and the way they are sampled; the introduction of covariates and qualitatives issues are important as well. Those features al need a specific probabilistic work which involves tightly the subjacent dependence structures. Statistical and computational issues are essential for data studies. My aim is now to deal with applications to various disciplines where it makes sense, such as insurance, astronomy, and others.
Ecology is the most challenging application to dependence to which I will dedicate from now on.