Rémi Viola

Welcome to my professional web page.

Short bio

I was a Mathematics teacher in middle and high school between September 2005 and August 2018.

Between September 2016 and August 2018, I have done a Master degree in Computer Science in Machine Learning & Data Mining Master. During this Master, I did two research internships at Hubert Curien Laboratory. The first one was under the supervision of Dr. Rémi Emonet on "Subspace Alignment for Domain Adaptation with Imbalanced classes". The second one was under the supervision of Pr. Marc Sebban and Pr. Amaury Habrard on "New methods of anomaly detections by Machine Learning from highly unbalanced data".

I am now a doctoral researcher in Computer Science at University Jean Monnet of Saint-Etienne (France) since September 2018.

I work under the supervision of Pr. Marc Sebban and Pr. Amaury Habrard at Hubert Curien Laboratory in the Data Intelligence team and at the French Ministry of Economy and Finance.

My PhD is funded by the French Ministry of Economy and Finance. Its subject is on Fraud detection.

Research Interests:

Teaching Activities

  • 2016-2020 : Practical Sessions of Latex in Bachelor(1st year)
  • 2018-2019 : Practical Sessions of Python in Bachelor(1st year)
  • 2014-2018 : Computer initiation in High School
  • 2010-2018 : Mathematics in High School
  • 2005-2010 : Mathematics in Middle School

Science Popularization

  • 09/2019 : European Researchers' Night
  • 05/2017 : Ramène ta Science

Publications

  • A Nearest Neighbor Algorithm for Imbalanced Classification.[link]
    Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban
    International Journal on Artificial Intelligence Tools (IJAIT), 2021
  • Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data.[link]
    Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban
    Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020
  • An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data.[link]
    Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban
    31th International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, 2019

Code

  • Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data.[link]
  • An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data.[link]