Graham Taylor

Headshot of Graham Taylor
Professor, Tier 2 Canada Research Chair in Machine Learning
School of Engineering
Phone number: 
(519) 824-4120 ext. 53644
THRN 3515
Available positions for grads/undergrads/postdoctoral fellows: 
Enquire by email


20 NVIDIA RTX 2080 Ti GPUs with 11GB memory each, spread over 3 servers (local)
24 NVIDIA T4 GPUs with 16GB memory each, spread over 6 servers (hosted by Compute Canada)
Access to more than 11,000 GPUs through the Vector Institute for Artificial Intelligence


We use GPUs to accelerate training and inference of deep learning models across a wide variety of applications.

Education and Employment Background

Dr. Graham Taylor received his PhD from the University of Toronto in 2009. Taylor joined the University of Guelph in 2012, where he is currently an Associate Professor in the School of Engineering. He holds a Canada Research Chair in Machine Learning, is the Academic Co-Director for the University of Guelph’s Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), leads the Machine Learning Research Group at the U of G, is the Academic Director for NextAI, and is a member of the Vector Institute for Artificial Intelligence.

Research Themes

Taylor’s research is focused on deep learning and representation learning, with an emphasis on unsupervised learning and sequential data. He has applied his research to problems in computer vision, graphics, weather modeling, finance, and motion capture-based crowd games. Key areas of focus include:

  1. Novel Architectures and Algorithms. Taylor explores automatic construction of hierarchical algorithms from high-dimensional, unstructured data. He is particularly interested in time series and has applied his work in many different spheres, including human and animal behaviour, environmental data, audio, and financial time series.
  2. Applications that benefit society. AI has the potential to have far-reaching societal and economic implications. Taylor’s work with the U of G’s Centre for Advancing Responsible and Ethical Artificial Intelligence aims to ensure that AI technologies benefit people and minimize harm.
  3. Exploiting hardware accelerators to scale the training and deployment of machine learning systems. Taylor seeks to make hardware accelerated computing more effective and accessible for machine learning scientists and practitioners. He aims to find better ways to cope with the challenges of large-scale machine learning.


  • Canada CIFAR (formerly the Canada Foundation for Advanced Research) AI Research Chair, 2018
  • Google Visiting Faculty, Google, 2018
  • Canada’s Top 40 Under 40, Caldwell Partners, 2017
  • Azrieli Global Scholars, CIFAR, 2016
  • Guelph 40 Under 40, Guelph Life, 2016

Media Coverage

Computer Vision

Radio Netherlands Worldwide: Computer Vision meets Dutch Music Video. Radio Netherlands Worldwide


Generative Systems

General Research

Ethics and AI


CBC Radio 1: AI and Voice Activated Devices

Guelph Life: The Human Side of Machine Learning

Businesses and AI

Agriculture and AI

Awards and Accolades

The National Post: The Power of Top 40 Under 40


Brain Drain

Green Energy

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