SITA'14 - 9th International Conference on Intelligent Systems:
Theories and Applications

07-08 May 2014, Rabat, Morocco

The special issue is now online. You can see the issue by clicling IJCSA published issues vl2I1, 2015

            Peter RICHTARIK

University of Edinburgh

         Peter Richtarik is an Assistant Professor of Mathematical Optimization at the University of Edinburgh. He received his PhD in Operations Research in 2007 from Cornell University and prior to his current appointment spent two years at the Center for Operations Research and Econometrics, Belgium, as a postdoctoral scholar, hosted by Prof Yurii Nesterov. He has recently spent a semester at the University of California, Berkeley, as an invited researcher, participating in the Theoretical Foundations of Big Data Analysis program run at the Simons Institute for the Theory of Computing.
       Dr Richtarik is an expert in stochastic methods for big data convex optimization. In a series of papers he developed and analyzed randomized coordinate descent methods of various flavours---serial, parallel, distributed, proximal, nonsmooth, accelerated and nonuniform. These methods are scalable to problems involving billions of variables. The algorithms (e.g., PCDM, Hydra, APPROX) were successfully implemented on serial and parallel architectures, ranging from single and multicore processors, through GPUs, to supercomputers. He has also worked on topics such as sparse principal component analysis (GPower), optimization in relative scale, cutting plane methods, image classification, truss topology design and optimal planet formation.
      The papers of Dr Richtarik have won several prizes, including the 16th Leslie Fox Prize (2nd prize), awarded biennially since 1985 to a numerical analyst worldwide under the age 31, and the 2012 INFORMS Computing Society Best Student Paper Prize (sole runner up). These prizes were awarded to his co-author and PhD student Martin Takac. Dr Richtarik has delivered more than 80 research talks in many countries around the world. He is often invited to give keynote and plenary addresses at events focused on the theory and practice of big data analysis, machine learning, computational statistics and optimization. He has organized several international scientific workshops and symposia.
      Dr Richtarik is a member of the Edinburgh Research Group in Optimization, Edinburgh Compressed Sensing and the Algorithms and Complexity Research group. He serves on the steering committees of the Centre for Doctoral Training in Data Science and the Centre for Numerical Algorithms and Intelligent Software, and is affiliated with the Maxwell Institute Graduate School in Mathematical Analysis and Applications --- all three being institutions funded by £5m grants from the UK government. His recent research is funded by grants from EPSRC and other sources totalling more than £1m. He is a member of the EPSRC Peer Review College, and serves as an evaluator of EU proposals.


University of Montreal

    El Mostapha Aboulhamid is a professor at Université de Montréal, since 1985. He obtained his Engineering degree from ENSIMAG Grenoble in 1974 and his Ph.D. from Université de Montréal in 1984. He is active in modeling, synthesis and verification of hardware systems. His pioneer work in the 1980s and early 1990s on built-in-self test techniques, design for testability, multiple fault automatic test generation and complexity of test is still referenced by different researchers.
      He was involved in the current methodology of design of hardware/software systems since its early beginnings in the 1980s and 90s with the introduction of VHDL. He helped to the acceptance of this methodology in Canada, by the collaboration with industrial partners and by delivering intensive courses on modeling and synthesis both in academia and industrial settings, nationally and internationally. His current interests are in system level modeling, parallel simulation and computing acceleration using different parallel paradigms. He has more than 150 publications in conferences and journals.

Bernadette Bouchon-Meunier

UPMC - LIP6BC 169, 4 place Jussieu
75252 Paris Cedex 05, France
Phone (33) 1 44 27 70 03
Fax (33) 1 44 27 70 00

    Bernadette Bouchon-Meunier est directeur de recherche émérite au Centre National de la Recherche Scientifique. Elle a été responsable du département Bases de Données et Apprentissage Artificiel du Laboratoire d'Informatique de Paris 6 (LIP6) à l'Université Pierre et Marie Curie-Paris 6 jusqu'en 2013.
      Ancienne élève de l'Ecole Normale Supérieure de Cachan, titulaire de maîtrises de Mathématiques et d'Informatique et d'un doctorat de 3ème cycle en Mathématiques Appliquées, elle est Docteur en Sciences de l' Université Paris 6.
      Editeur-en-chef de l'International Journal of Uncertainty, Fuzziness and Knowledge-based Systems publié par World Scientific, elle est aussi membre du comité éditorial des revues internationales International Journal of Approximate Reasoning, Fuzzy Sets and Systems, Journal of Uncertain Systems. Elle a (co)-dirigé vingt-quatre livres et est le (co)-auteur de quatre livres en français et un livre en vietnamien sur la logique floue et le traitement d'incertitude en intelligence artificielle. Elle est aussi directeur de la collection "Recherche d'Information et web" publiée par Hermes Science Pub. Co-fondatrice et co-directeur exécutif de la conférence internationale sur le Traitement d'Information et la Gestion d'Incertitude dans les Systèmes à base de connaissances (IPMU) qui a lieu tous les deux ans depuis 1986.
       Elle a été membre élu du Bureau Administratif de l'IEEE Computational Intelligence Society de 2004 à 2009 et de 2011 à 2014, membre nommé de l'IEEE Women in Engineering committee en 2007-2008 et de 2012 à 2014. Elle est actuellement présidente du chapitre Computational Intelligence de la section française de l'IEEE. Membre du IEEE Women in Computational Intelligence Committee, elle l'a présidé de 2004 à 2007. Elle est fellow de l'IEEE et fellow de l' International Fuzzy Systems Association. Elle a reçu la IEEE Computational Intelligence Society Meritorious Service Award en 2012.
       Ses recherches actuelles concernent le raisonnement approximatif et basé sur des similarités, ainsi que les applications de la logique floue et de techniques d'apprentissage automatique à l'aide à la décision, la fouille de données, la prévision de risques, la recherche d'information, la modélisation de l'utilisateur et le traitement d'informations sensorielles et émotionnelles.