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 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. Before this I introduced wavelets in statistics, which had important developments. In addition to my recent elementary book on times series modeling, the use of integer valued models and high dimensionality are needed to deal with real data sets. More features of real data such as non stationarity (e.g. local stationarity, isotonic or explosive behaviours), the way they are sampled, the introduction of covariates and more 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. Applications of dependence to deal with are significant in various disciplines such as insurance, astronomy, and others. Ecology is one essential problem of the century, to which I will dedicate in Ecodep with my dependence skills.