About

I am an associate professor in artificial intelligence and deep learning at the University of Liège, Belgium. Before that, I was a postdoctoral associate at New York University with the Physics Department and the Center for Data Science.

I do research at the intersection of machine learning, artificial intelligence and physical sciences, with close ties with the ATLAS experiment at CERN. My far ambition is to unlock discoveries of a new kind by making artificial intelligence a cornerstone of the modern scientific method. Using particle physics as a test bed, my present research interests circle around how to use or design new machine learning algorithms to approach data-driven scientific problems in new and transformative ways. With this goal in mind, my current topics of research include:

  • simulator-based likelihood-free inference
  • deep generative models
  • probabilistic programming
  • developments towards the automation of sience

Curriculum vitae.

Publications

See also Google Scholar or ORCID.

2017

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

2018

  • Adversarial Games for Particle Physics

2017

2016

2015

2014

2013

2012

2011

  • Large-scale machine learning for collaborative filtering

2010

  • A zealous parallel gradient descent algorithm

Teaching

2017-2018