Skip to content
CIFAR header logo
fr
menu_mobile_logo_alt
  • Our Impact
    • Why CIFAR?
    • Impact Clusters
    • News
    • CIFAR Strategy
    • Nurturing a Resilient Earth
    • AI Impact
    • Donor Impact
    • CIFAR 40
  • Events
    • Public Events
    • Invitation-only Meetings
  • Programs
    • Research Programs
    • Pan-Canadian AI Strategy
    • Next Generation Initiatives
  • People
    • Fellows & Advisors
    • CIFAR Azrieli Global Scholars
    • Canada CIFAR AI Chairs
    • AI Strategy Leadership
    • Solution Network Members
    • Leadership
    • Staff Directory
  • Support Us
  • About
    • Our Story
    • Awards
    • Partnerships
    • Publications & Reports
    • Careers
    • Equity, Diversity & Inclusion
    • Statement on Institutional Neutrality
    • Research Security
  • fr
  • Home
  • Bio

Follow Us

Danica J. Sutherland

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

Website

About

Appointed Canada CIFAR AI Chair – 2021

The recent success of machine learning has been in large part due to massive success in an area known as representation learning, where a computer algorithm finds a way to identify structure in data. Danica Sutherland’s research focuses on improvements to the process of representation learning, especially using ideas from a tool known as kernel methods. Integrating kernels into currently-popular approaches can, ideally, help learn effective representations with smaller training datasets, and which generalize well even to populations different from those used in training. A major line of her research focuses on representations which identify differences between datasets, such as whether medical images differ between treatment and control groups, or if a generative model has succeeded at matching its goal distribution. She tries to work both on practical problems informed by theoretical viewpoints, and on theoretical problems informed by practice.

Relevant Publications

  • Kamath, P., Tangella, A., Sutherland, D.J., & Srebro, N. (2021). Does Invariant Risk Minimization Characterize Invariance? Artificial Intelligence and Statistics.

  • Zhou, L., Sutherland, D.J., & Srebro, N. (2020). On Uniform Convergence and Low-Norm Interpolation Learning. Advances in Neural Information Processing Systems.

  • Liu, F., Xu, W., Lu, J., Zhang, G., Gretton, A., & Sutherland, D.J. (2020). Learning Deep Kernels for Non-Parametric Two-Sample Tests. International Conference on Machine Learning.

  • Bińkowski, M., Sutherland, D.J., Arbel, M., & Gretton, A. (2018). Demystifying MMD GANs. International Conference on Learning Representations.

  • Sutherland, D.J. & Schneider, J. (2015). On the Error of Random Fourier Features. Uncertainty in Artificial Intelligence.

Institution

Amii

University of British Columbia

Department

Computer Science

Education

  • PhD and MS (Computer Science), Carnegie Mellon University
  • BA (Computer Science), Swarthmore College

Country

Canada

Support Us

The Canadian Institute for Advanced Research (CIFAR) is a globally influential research organization proudly based in Canada. We mobilize the world’s most brilliant people across disciplines and at all career stages to advance transformative knowledge and solve humanity’s biggest problems, together. We are supported by the governments of Canada, Alberta and Québec, as well as Canadian and international foundations, individuals, corporations and partner organizations.

Donate Now
CIFAR header logo

MaRS Centre, West Tower
661 University Ave., Suite 505
Toronto, ON M5G 1M1 Canada

Contact Us
Media
Careers
Accessibility Policies
Supporters
Financial Reports
Subscribe

  • © Copyright 2025 CIFAR. All Rights Reserved.
  • Charitable Registration Number: 11921 9251 RR0001
  • Terms of Use
  • Privacy
  • Sitemap

Subscribe

Stay up to date on news & ideas from CIFAR.

This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember you. We use this information in order to improve and customize your browsing experience and for analytics and metrics about our visitors both on this website and other media. To find out more about the cookies we use, see our Privacy Policy.
Accept Learn more