Hi ! I'm Dylan.

I studied robots and their brains at EPFL in Switzerland,
and defended my Masters' Thesis at Stanford.
I am currently building something new!
You can check out my CV or get in touch here.


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 legal frameworks

D. Bourgeois, S. Vergnolle   //   PLSC'22

Learning Representations of Source Code from Structure and Context

D. Bourgeois   //   MSc. Thesis

GNNExplainer: Generating explanations for Graph Neural Networks

R. Ying, D. Bourgeois, J. You, M. Zitnik, J. Leskovec   //   NeurIPS'19

A Dynamic Embedding Model of the Media Landscape

J. Rappaz, D. Bourgeois, K. Aberer   //   WWW'19

Selection Bias in News Coverage: Learning It, Fighting It

D. Bourgeois, J. Rappaz, K. Aberer   //   WWW'18

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   //   JHEP'18


Cleaning Robot

R. Brooks, D. Bourgeois, C. Chao, A. Trevor, M.R. Amer, A. Jules, G.F. Marcus   //   Published 2021-11-11

Ultraviolet cleaning trajectory modeling

A. Trevor, D. Bourgeois, M. Kollmitz, C. Chao   //   Published 2021-11-11