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Geert Verdoolaege
Geert Verdoolaege

Geert Verdoolaege

Assistant Professor

Sint-Pietersnieuwstraat 41, Technicum B4

B-9000 Gent, Belgium


Geert.Verdoolaege@UGent.be


T: +32 9 264 9591
F: +32 9 264 4198

Education

 

    • MS, Theoretical Physics, Ghent University, 1999
    • PhD, Engineering Sciences (Applied Physics), Ghent University, 2006

      Background

       

      • Graduated in theoretical physics in 1999 at Ghent University.
      • PhD degree in engineering physics in 2006, with research on Bayesian integrated data analysis for the estimation of the ion effective charge in tokamak plasmas, at Ghent University.
      • Joined the Vision Lab at the Department of Physics of the University of Antwerp (2007-2008). There he worked on the analysis of stochastic images in the framework of information geometry.
      • From 2008 until 2010: postdoctoral assistant in the Department of Data Analysis at Ghent University, working on the application of probability theory and signal processing to fMRI data analysis.
      • In 2010 Geert Verdoolaege returned to the Department of Applied Physics at Ghent University as a postdoctoral assistant, continuing his work on advanced fusion data analysis.
      • Since October 2013 he has a double affiliation to Ghent University and to the Laboratory for Plasma Physics at the Royal Military Academy (ERM/KMS) in Brussels.
      • From February 2014 onwards, in addition to his appointment at ERM/KMS, he became a part-time assistant professor in the department of Applied Physics at Ghent University.
      • At Ghent University, he has been teaching a master course on Continuum Mechanics since 2011 and he co-teaches a course on Plasma Physics.

      Research interests

       

          • Plasma confinement, instabilities and turbulence
          • Scaling laws
          • Bayesian probability theory, maximum entropy methods and machine learning
          • Information geometry

              Research description

              Geert Verdoolaege leads the group on Advanced Fusion Data Analysis at Ghent University. He has been working on various topics related to the foundations and applications of probability theory and machine learning methods since 1999. He was among the first to use various modern data analysis techniques in fusion science and he introduced the field at Ghent University. He first concentrated primarily on integrated data analysis for fusion diagnostics using Bayesian probability theory, later also including probabilistic modeling and analysis of textured images in remote sensing applications and functional brain scans obtained from magnetic resonance imaging. In addition, Geert Verdoolaege initiated research on pattern recognition in fusion data, modeled as probability distributions on information manifolds. His group develops new probabilistic techniques for analyzing highly stochastic fusion data and phenomena, with current applications to plasma instabilities (ELMs and disruptions), plasma turbulence and fusion scaling laws. Further activities include Bayesian analysis of fusion diagnostics with application to reflectometry and charge-exchange spectroscopy, and Bayesian real-time tomography for soft-X-ray spectroscopy.

              Teaching

              Graduate courses

              • Continuum Mechanics (Mechanica van Continue Media, E040430 and C002676), instructor
              • Plasma Physics (Plasmafysica, E026220), co-teacher

              Publications

               

               

                  Other

                   

                  • Member of the editorial board of the journal Entropy (IF 1.502)
                  • Expert in the International Tokamak Physics Activity (ITPA) Topical Group on Transport and Confinement
                  • Member of the Scientific Board of the Erasmus Mundus Joint Doctorate Programme “International Doctoral College in Fusion Science and Engineering” (FUSION-DC)
                  • Member of the General Assembly of the FuseNet Association (European Fusion Education Network)
                  • IEEE member (since 2007)