Guillaume Metzler

I am currently Assistant Professor in Computer Science at the University Lumière Lyon 2 since September 2020.
I am a member of the Data Mining & Decision Team (DMD) in the ERIC Laboratory located on the Campus of Bron. I am also co-responsible of Scientific Seminars in the laboratory.

I am working in the field of Machine Learning and I particularly on the following topics: Learning From Imbalanced Data - Metric Learning - Cost Sensitive Learning and Boosting. More recently, I am also interested by the topic of Domain Adaptation.

I am teaching for the Institute of Communication (ICOM) on the Porte des Alpes campus of Lyon 2 University. I am giving classes in the field of Statistics, basics in Informatics and in the Machine Learning area for Bachelor and Master students.

I am also giving some classes in Statistics and Data Analysis for Bachelor Students at EM Lyon Business School.

I have studied Fundamentals and Applied Mathematics in the University of Strasbourg where I obtained my Bachelor and Magistère. Previously graduated from the University of Lyon in Applied Mathematics in Biology in Medecine I have done a PhD in Computer Science at the Hubert Curien Laboratory (from January 2016 to September 2019) under the supervision of Pr. Marc Sebban and with the Blitz Business Services Company on Fraud Detection. After that, I have done a 10 months post-doc at the Hubert Curien Laboratory under the supervision of Pr. Amaury Habrard.

A Nearest Neighbor Algorithm for Imbalanced Classification, R.Viola, R.Emonet, A.Habrard, G.Metzler and M.Sebban
Keywords: Nereast Neighbors - Imbalanced Classification - Cost-Sensitive Learning

Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting, L.Gautheron, P.Germain, A.Habrard, G.Metzler, E.Morvant, M.Sebban and V.Zantedeschi.
Keywords: Classification - Gradient Boosting - Random Fourier Features - Kernel Approximation

Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data, R.Viola, R.Emonet, A.Habrard, G.Metzler and M.Sebban
Keywords: Classification - Metric Learning - Generalization Bound .