Selected Publication

This paper presents an instance-based algorithm allowing exploration of large medical dataset by making pairwise connection between patients. In our metric-free method, each individual in a dataset ranks every member of the dataset. By aggregating these ranks, it is then possible to visualize data according to typical individuals representing subsets of closely-related patients. The paper also describes a visualization tool allowing exploration of a database of diabetic patients. This prototype of a recommendation system implements the aforementioned algorithm to enrich data, structure patients, create associations between individuals and provide recommendations.
KES, 2017



An instance-based visualization and recommendation system for medical data.


Teaching assistant at the University Institute of Technology in Reims (France).

I currently teach the following computer science courses:

  • Introduction to computer science and Linux
  • Introduction to object-oriented programming
  • Introduction to R programming
  • Object-oriented Java programming
  • Advanced algorithms