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--- en:start [2012/06/20 10:56] – quostben
+++ en:start [2024/01/22 11:31] (current) – quostben
@@ Line 1: / Line 1: @@
-{{fr:benjamin_quost.jpg?150 |Benjamin Quost }}
+{{fr:benjamin_quost.jpg?185 |Benjamin Quost }}
+**Benjamin Quost** --- Associate professor with habilitation \\
-**Benjamin Quost** --- Maître de Conférences \\
+Royallieu research center, dept. Computer Science \\
-C.R. Royallieu, bureau C119 \\
+Pascal building, office 201 \\
-tél. : +33 (0)3 44 23 49 68 \\
+tel. : +33 (0)3 44 23 49 68 \\
 fax : +33 (0)3 44 23 44 77 \\
 [[benjamin.quost@hds.utc.fr|benjamin.quost@hds.utc.fr]]
 [[http://www.utc.fr|Université de Technologie de Compiègne]] \\
-Centre de Recherches de Royallieu, département Génie Informatique \\
+Rue Roger Couttolenc, 60200 Compiègne
-Laboratoire [[http://www.hds.utc.fr|HeuDiaSyC]], UMR UTC-CNRS 6599 \\
-BP 20529, F60205 Compiègne Cedex
+[[http://www.hds.utc.fr|Laboratoire Heudiasyc]], UMR UTC-CNRS 7253 \\
+Av. de Landshut, 60200 Compiègne
 \\
 Welcome on my web page. \\
 You will find here a summary of my professional activities. \\
-For any other information, have a look at {{fr:20080602_CV_Benjamin_Quost.pdf|my vitae}}, and do not hesitate to contact me !
+For any other information, do not hesitate to contact me !
-====== News ======
-The Web site of the [[http://www.hds.utc.fr/epat2010/|EPAT]], a spring school dedicated to machine learning, was updated ! \\
-The school is now open to applications.
@@ Line 35: / Line 25: @@
 ====== Brief presentation ======
-I am now associate professor at the university of Technology of Compiègne.
+I am now associate professor (habil.) at the Université de Technologie de Compiègne.
-My research focuses on machine learning, and more particularly supervised and unsupervised classification, from imperfect (imprecise, uncertain) information. I also worked on data fusion.
-I am interested in the theoretical framework of belief functions (also known as Dempster-Shafer theory) for representing and managing imprecision and uncertainty.
+My research focuses on machine learning, and more particularly on learning cautious models from scarce or poor data (Safe AI) or under fairness constraints; on explainability, and in particular explaining indeterminate predictions; and on decision-making from partial information. I also work on classifier combination and data fusion issues.
+I am interested in the theoretical frameworks of probability theory, belief functions (also known as Dempster-Shafer theory), and imprecise probability, for representing and managing imprecision and uncertainty.

UMR CNRS 7253

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