Didier Leibovici

Dr Didier G. Leibovici is a Research Fellow in Geocomputational Statistics & GIS at the University of Leeds. He has a PhD in Applied Mathematics from the University of Montpellier II and worked for some years as a Statistician Researcher in epidemiological and medical research contexts in France and in England. Dr Leibovici also has a Master’s degree in Information Technology from the University of Montpellier II and his previous posts were in geomatic modelling for landscape changes at the Institute of Research for Development (IRD) in France, and then in geospatial modelling and analysis at the Nottingham Geospatial Institute at the University of Nottingham .

He has expertise in statistics with spatial and non-spatial data, data modelling and GIS application modelling, within the context of interoperable Spatial Data Infrastructures. Dr. Leibovici applied these expertise in various European projects such as the FP6 program on Desertification (DeSurvey) and more recently in the eSoTer FP7 “Soil and Terrain database platform for the European Union“ (contributing to data modelling and OGC/ISO/INSPIRE standards, and meta-modelling for data processing), GIS4EU part of eContent+ European program (contributing to data modelling specification),  the FP7 GIGAS, “GEOSS, INSPIRE, GMES, Action in Support” for convergence of the initiatives, and  the FP7 EuroGEOSS “European approach to GEOSS” (contributing within the project to multi-scale and integrated modelling controls of a GEOSS model workflows).

His research interests are related to interoperability and conflation models in geospatial analysis and integrated modelling applications, particularly in the context of spatial data infrastructures such as GEOSS. This translates to a focus on geospatial statistics, geospatial patterns, outbreak detection and geospatial data mining in general, but also to data quality and uncertainty propagation principles in relation to geoworkflows connected to/using web services. Didier’s research centres on environmental agro-ecological geospatial models, and public health and spatial epidemiology applications.