About

Hi there! My name is Gilles. Working from the CERN laboratory near the Swiss Alps, I am currently a Postdoctoral Associate at NYU with the Physics Department and the Center for Data Science. I do research in machine learning for high energy physics data, in close collaboration with Kyle Cranmer and the ATLAS experiment. Before that, I was a Doctoral Student at the University of Liège in Belgium.

My interests broadly include fundamental statistical inference problems, artificial intelligence, optimization algorithms, and their application to scientific disciplines like particle physics. As part of the DIANA-HEP project, I am also deeply interested in engineering good scientific software.

Curriculum vitae.

Publications

See also Google Scholar or ORCID.

2016

2015

  • Pitfalls of evaluating a classifier’s performance in high energy physics applications
  • Ethnicity sensitive author disambiguation using semi-supervised learning
  • Collaborative analysis of gigapixel images using Cytomine
  • Scikit-learn: Machine Learning Without Learning the Machinery
  • Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

2014

2013

2012

2011

  • Learning to rank with extremely randomized trees

2010

  • A zealous parallel gradient descent algorithm
  • Collaborative filtering: Scalable approaches using restricted Boltzmann machines

Talks

2016

2015

2014

2013

2012

2011

  • Large-scale machine learning for collaborative filtering

2010

  • A zealous parallel gradient descent algorithm

Software

Scikit-Learn

Scikit-Learn is an easy-to-use and general-purpose machine learning library written in Python. It integrates classic machine learning algorithms within the scientific Python ecosystem (numpy, scipy, matplotlib). I am an active developper since 2011.

Carl

Carl is a toolbox for likelihood-free inference in Python.