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Didier Dubois, INRIT-CNRS, Université Paul Sabatier
The variability of physical phenomena and partial ignorance about them motivated the development of probability
theory in the two last centuries. However, the mathematical framework of probability theory, together with the Bayesian credo claiming the inevitability of unique probability measures for representing agents' beliefs, have blurred the distinction between variability and ignorance. Modern theories of uncertainty, by putting together probabilistic and set-valued representations of information, provide a better account of the various facets of uncertainty.
Speaker biosketch
Didier Dubois is professor emeritus at Université Paul Sabatier in Toulouse, France, and research director (directeur de recherche) at the Centre national de la recherche scientifique (CNRS), at the Institut de Recherche en Informatique de Toulouse. He was educated as a civil engineer of aeronautics at the French National School of Aeronautics and Space from which he also earned a doctorate in engineering. His second doctorate (docteur d'état) is from the Scientific and Medical University of Grenoble in mathematical models of the imprecise and the uncertain. Dubois served as president of the International Fuzzy Systems Association, and is the longtime editor-in-chief of Fuzzy Sets and Systems. He has worked at the IMAG Institute (Bâtiment IMAG) in Grenoble, Purdue University in the United States, the Centre d'Études et de Recherche de Toulouse, Departement d'Études et de Recherche en Automatique, and the Languages and Computer Systems Laboratory at Paul Sabatier with the artificial intelligence and robotics team. He received the Pioneer Award from the IEEE Neural Network Society, and a doctor honoris causa from the Faculté Polytechnique de Mons (Belgium). He is a fellow of the International Fuzzy Systems Association, and was named one of the 300 most cited French scientific authors by Institute for Scientific Information.
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