CV

Personal data

  • Professional situation : Postdoctoral Researcher, University of Lorraine (LORIA lab)
  • Address : Campus Scientifique, 615 Rue du Jardin-Botanique, 54506 Vandœuvre-lès-Nancy, France
  • Phone : +33 (0) 6 43 82 88 46
  • Email : akrem.sellami@inria.fr ; akrem.sellami@telecom-bretagne.eu

    Education

    • 2014-2017 : PhD in Signal, Image, Vision  (ITI / IMT Atlantique de Brest)
    • Subject: Semantic interpretation of hyperspectral images based on an adaptive reduction of dimensionality
      Advisors: Basel Solaiman and Imed Riadh Farah
      Defense date: December 11, 2017
      Distinction: Very Honorable

    • 2012: MSc indegree in Computer Science (RIADI / Université de Jendouba)
    • Subject: Integration of spatio-temporal relationships in Hidden Markov Chains for intra-urban monitoring
      Advisors: Imed Riadh Farah and Vincent Barra
      Internship: Geometry, Image, Learning, Algorithm Group, LIMOS laboratory, Blaise Pascal University, Clermont-Ferrand, France
      Defense date: December 21, 2012
      Distinction: Very Good

    WORK EXPERIENCE

    • June 2020- Present: Postdoctoral Researcher – Université de Lorraine – Vandœuvre-lès-Nancy

      Research Group: D4. Traitement automatique des langues et des connaissances (LORIA)

    • Oct 2018- May 2020 : Postdoctoral Researcher, Aix-Marseille University, France
      Research Group: Qarma team (apprentissage automatique de Marseille, LIS lab) and Banco team (INT)

    • 2017-2018: Research Assistant (ATER), Paris Descartes University, France- IUT
    • Research Group: SOCS team (LIPADE)

    • Aug.2012-Dec.2012: Research Internee, Blaise Pascal University, Clermont-Ferrand, France
      Research Group: G4 team (LIMOS)

    Research Area and Skills

    Deep Learning
    80%
    Computer Vision
    75%
    Graph Deep Representation Learning
    70%
    Multi-view Learning
    70%
    Dimensionality Reduction
    80%

    My research area include machine learning (deep learning, representation learning,…), graph deep representation learning, dimensionality reduction (features extraction/selection, multi-linear algebra), data analytics, as well as applications to brain/satellite images.