Barthelemy, Johan Dr.

Research Fellow

  • Research Fellow - SMART Infrastructure Facility

Overview


Complex systems, such as social networks, the brain, financial systems, and infrastructure networks are composed of a large number of entities in interaction and exhibiting emerging phenomena. The development of microsimulations using agent-based modelling techniques is then often crucial to model, analyze and simulate such complex systems.

Dr Johan Barthélemy is an early career researcher at SMART focusing on the development of new tools and frameworks to simulate large scale agent-based microsimulation. This include i) the design of synthetic population generators to build a disagregated database of the system’s entities; and ii) the development of microsimulations to simulate and observe the agents’ behaviour. Applications of this work include transportation modelling and disease spreading which can involve millions of agents. The research is interdisciplinary and requires methods from applied mathematics, statistics and high performance scientific programming.

In addition, he is the director of the SMART's Internet of Things Laboratory where he develops sensors for various applications such a smart beer kegs, water level monitoring and low cost gas sensing.

Its collaboration with NIASRA also led to the development of mipfp, a widely-used statistical package for the R programming language.

Finally, as a member of the Namur Center For Complex Systems (University of Namur) he collaborates on projects related to algorithmic complexity, data analytics and agent-based modelling.

Top Publications


Available as Research Supervisor

Selected Publications


Education And Training


  • Ph.D. in Applied Mathematics, University of Namur, Department of Mathematics, Ph.D. Thesis: A parallelized micro-simulation platform for population and mobility behaviour - Application to Belgium 2007 - 2014
  • M.Sc. in Mathematics, University of Namur, Department of Mathematics, Master thesis: Contribution of a new dissimilarity measure to a graph-based segmentation algorithm 2005 - 2007
  • B.S. in Applied Mathematics, University of Namur, Department of Mathematics 2003 - 2005

Teaching Overview


  • Altough my position is research only, I had the opportunity to teach 2 separates courses at the University of Namur (Belgium) as an invited lecturer in 2016 and 2017. Those courses are briefly described here under.

    1. Multivariate Data Analysis and Statistical softwares [SMATM102]:
    The course is an introduction to data analysis. The course firstly presents the statistical methods adapted to the univariate data. Then, the various methods of multivariate statistical analysis are presented, in a theoretical and practical way: ANOVA, Contingency Table, Linear Models, Regression, Clustering, Principal Component Analysis and Factor Analysis.

    2. Special Questions of Mathematics - Introduction to Data Science with Python and SQL [SMATM130]:
    This course aims to give tools and methods to conduct data analytics using programming languages widely used and freely available, namely Python 3 and SQL. This introduction to data science details briefly the following topics: data manipulation, data analysis with statistic and machine learning, data visualization and how to work with large data sets.

    The 2 courses are part of the curriculum for the Master's Degree in Applied Mathematics. For both courses, I designed and prepared the material, delivered the courses content to the student and evaluated the learning activities. This included lectures, lab classes, project supervision, designing assessments, assesments and markings.

Keywords


  • Mathematical modelling and simulation; Agent-based simulation; High performance and scientific computing; Statistics and probability; Synthetic population generation.

Web Of Science Researcher Id


  • M-1704-2017

Top Publications


Selected Publications


Education And Training


  • Ph.D. in Applied Mathematics, University of Namur, Department of Mathematics, Ph.D. Thesis: A parallelized micro-simulation platform for population and mobility behaviour - Application to Belgium 2007 - 2014
  • M.Sc. in Mathematics, University of Namur, Department of Mathematics, Master thesis: Contribution of a new dissimilarity measure to a graph-based segmentation algorithm 2005 - 2007
  • B.S. in Applied Mathematics, University of Namur, Department of Mathematics 2003 - 2005

Teaching Overview


  • Altough my position is research only, I had the opportunity to teach 2 separates courses at the University of Namur (Belgium) as an invited lecturer in 2016 and 2017. Those courses are briefly described here under.

    1. Multivariate Data Analysis and Statistical softwares [SMATM102]:
    The course is an introduction to data analysis. The course firstly presents the statistical methods adapted to the univariate data. Then, the various methods of multivariate statistical analysis are presented, in a theoretical and practical way: ANOVA, Contingency Table, Linear Models, Regression, Clustering, Principal Component Analysis and Factor Analysis.

    2. Special Questions of Mathematics - Introduction to Data Science with Python and SQL [SMATM130]:
    This course aims to give tools and methods to conduct data analytics using programming languages widely used and freely available, namely Python 3 and SQL. This introduction to data science details briefly the following topics: data manipulation, data analysis with statistic and machine learning, data visualization and how to work with large data sets.

    The 2 courses are part of the curriculum for the Master's Degree in Applied Mathematics. For both courses, I designed and prepared the material, delivered the courses content to the student and evaluated the learning activities. This included lectures, lab classes, project supervision, designing assessments, assesments and markings.

Keywords


  • Mathematical modelling and simulation; Agent-based simulation; High performance and scientific computing; Statistics and probability; Synthetic population generation.

Web Of Science Researcher Id


  • M-1704-2017
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