I’m a PhD student in the Reasoning and Learning Lab at McGill University, supervised by Joelle Pineau.

I’m interested in many different areas of machine learning and cognitive science. Most recently I’ve been interested in ideas revolving around using reinforcement learning and other forms of stochastic optimization to train neural networks to extract and reason about discrete, structured representations such as objects and relations.

In 2014 I completed a Masters degree in Computer Science in the Computational Neuroscience Research Group at the University of Waterloo. I was supervised by Chris Eliasmith, and worked on a biologically plausible model of human knowledge representation. I also wrote an MPI implementation of the nengo neural simulator. In 2012 I obtained a BMATH(CS) degree, also from Waterloo, and spent my co-op terms working on a GPU implementation of nengo.

You can download my CV here.

Conference / Journal Articles

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks.
Eric Crawford and Joelle Pineau.
AAAI (2019). [code] [supplementary]

BanditSum: Extractive Summarization as a Contextual Bandit.
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof and Jackie C.K. Cheung.
EMNLP (2018).

Modeling interactions between speech production and perception: speech error detection at semantic and phonological levels and the inner speech loop.
Bernd J. Kroger, Eric Crawford, Trevor Bekolay and Chris Eliasmith.
Frontiers in Computational Neuroscience (2016).

Biologically plausible, human-scale knowledge representation.
Eric Crawford, Matthew Gingerich and Chris Eliasmith.
Cognitive Science (2015). [code]

Biologically plausible, human-scale knowledge representation.
Eric Crawford, Matthew Gingerich and Chris Eliasmith.
Conference of the Cognitive Science Society (2013). [code]

Workshops, Preprints, Theses, Reports

Spatially Invariant Attend, Infer, Repeat.
Eric Crawford and Joelle Pineau.
NeurIPS Workshop on Modeling the Physical World (2018). [code] [poster]

Sequential Coordination of Deep Models for Learning Visual Arithmetic.
Eric Crawford, Guillaume Rabusseau and Joelle Pineau.
arXiv preprint arXiv:1809.04988 (2017).

Policy Gradient Methods for Reinforcement Learning.
Eric Crawford. Ph.D. Comprehensive Exam, McGill University (2015).

Biologically plausible, human-scale knowledge representation.
Eric Crawford. Master of Mathematics Thesis, University of Waterloo (2015). [code]

Learning large-scale heteroassociative memories in spiking neurons.
Aaron Voelker, Eric Crawford and Chris Eliasmith.
Unconventional Computation and Natural Computation (2014).