Didier Leibovici

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Dr Didier Leibovici

Professional Title: Researcher
Other: Senior Research Fellow Geocomputational Modelling & Geospatial Statistics

Primary domain: geocomputational modelling and Spatial data Infrastructure

Organization name: University of Nottingham

Organization type: Academia/Research

City / Country: Nottingham - United Kingdom


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Dr Didier G. Leibovici is Senior Research Fellow in geocomputational modelling and geospatial statistics at the Nottingham Geospatial Institute, University of Nottingham. Dr Leibovici 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 Masters degree in Information Technology from the University of Montpellier II and worked in geomatic modelling for landscape changes at the IRD (Institute of Research for Development) in France. He has expertise in statistics with spatial and non-spatial data, data modelling and GIS application modelling and as such took part (whilst holding a project leader position in an IRD research unit) in a still ongoing FP6 program on Desertification (DeSurvey). Didier is taking part in the eSoTer FP7 “Soil and Terrain database platform for EU (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), and EuroGEOSS FP7 “European approach to GEOSS” (contributing within the project to multiscale and integrated modelling controls of a GEOSS model workflows), FP7 COBWEB on data quality information within a crowdsourcing or citizen science study. His research interests are related to interoperability, meta-information and spatial interaction in conflation models for cross-scales issues and to chaining/combining models or services for integrated modelling applications within an interoperable framework. This encompasses geocomputational optimisation, data quality and uncertainty impacts in relation to the workflow used together with sensibility assessments, also when modifying the workflow due to change of context e.g., upscaling, downscaling, in space and/or in time.