These publications span several concurrent domains, from the application of Machine Learning in High
Energy Physics (HEP) to
the development of novel characterizations of media bias.
This work is the product of diverse research interests
and a variety of research opportunities that I have been lucky enough to pursue.
The overarching goal is to provide interpretable methods, useful in their application but
that also provide a broader understanding of the problem at hand.
Explanations and meaningful information: at the interface between technical capabilities and
D. Bourgeois, S. Vergnolle
Learning Representations of Source Code from Structure and Context
GNNExplainer: Generating explanations for Graph Neural Networks
R. Ying, D. Bourgeois, J. You, M. Zitnik, J. Leskovec
A Dynamic Embedding Model of the Media Landscape
J. Rappaz, D. Bourgeois, K. Aberer
Selection Bias in News Coverage: Learning It, Fighting It
D. Bourgeois, J. Rappaz, K. Aberer
Using holistic information in the Trigger
D. Bourgeois, C. Fitzpatrick, S. Stahl
LHCb Public Note
New approaches for track reconstruction in LHCb's Vertex Locator
C. Hasse, J. Albrecht, B. Couturier, D. Bourgeois, V. Coco, N.
Nolte, S. Ponce