Elliot J. Crowley
pdb

I am a Senior Lecturer (Associate Professor) at the School of Engineering, University of Edinburgh. I co-lead the Bayesian and Neural Systems research group.

My research interests include:

  • simplifying machine learning
  • AutoML, especially neural architecture search
  • efficient network training
  • low-resource deep learning
  • engineering applications of machine learning

I have an MEng in Engineering Science and a DPhil (PhD), both from the University of Oxford. My DPhil was on "Visual recognition in Art using Machine Learning" with Andrew Zisserman in the VGG group. After my DPhil, I was a postdoc at the School of Informatics in Edinburgh with Amos Storkey.

I hold an EPSRC New Investigator Award. I am an investigator on the dAIEdge Horizon Network and a co-investigator on the EPSRC AI Hub for Causality in Healthcare AI.

I am Programme Manager for Electronics and Electrical Engineering at Edinburgh and I have written a machine learning course.

Team

Current: Former: If you are interested in starting a PhD with me, then please send me a targeted, nonverbose email (that doesn't read like it came out of an LLM) with your CV and a 1 page research proposal. At present, there are good sources of funding available for UK students e.g. through CDTs.

Carefully Selected Publications

einspace: Searching for Neural Architectures from Fundamental Operations

NeurIPS 2024

Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley

A new expressive search space for neural architecture search.

PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

BMVC 2024

Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, Elliot J. Crowley

A simple non-hierarchical state space model designed for general visual recognition.

Neural Architecture Search without Training

ICML 2021 (Long talk)

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

A low-cost measure for scoring networks at initialisation that can be used to perform neural architecture search in seconds.

Neural Architecture Search as Program Transformation Exploration

ASPLOS 2021 (Distinguished Paper)

Jack Turner, Elliot J. Crowley, Michael O'Boyle

A compiler-oriented approach to neural architecture search which can generate new convolution operations.

Thanks to Jack Turner and Chenhongyi Yang for the website template.