We develop predictive methods for the analysis of complex data, with application to Environmental and Medical data Analysis. Our vision is the integration of environmental and genomic information. Since 1996, we have been working on biological and environmental data, porting novel Statistical Machine Learning Methods (classifiers and regression models) within Geographical Information Systems (GIS). Our main applications are in landscape epidemiology and environmental risk analysis. More recently, we started developing Individual Based Model (IBM) simulators of global pandemic scenarios. We also develop predictive machine learning methods for functional genomics, since 2002. We have designed algorithms for predictive classification and profiling of high-throughput data (microarray and proteomics), with implementation on standard workstations, computer clusters and in grid. Our research is interdisciplinary: for our projects, we develop new sw infrastructures for data collection, management and analysis, supporting research collaborators and public agencies. We have created innovation in GIS (GRASS) and internet GIS (WebGIS) systems, and the spinoff company MPA Solutions in 2004. For high-throughput genomics data, we develop the complete validation setup BioDCV, several bioinformatics solutions, and we work for the integration of different genomics and patient data. Finally, we actively promote the dissemination of interdisciplinary science with the WebValley Summer School Project (previous editions),and by acting as a training lab for undergraduate and graduate students.