Researcher

Danica Sutherland

Also known as Danica J. Sutherland

Danica Sutherland is a machine learning researcher whose work spans generative modeling, optimal transport and language models. She completed a computer science Ph.D. at Carnegie Mellon in 2016, then worked at the Toyota Technological Institute at Chicago and the Gatsby Computational Neuroscience Unit before joining UBC in December 2020. She coauthored POT, an open-source optimal transport toolbox, and her 2025 ICLR paper on LLM fine-tuning won an Outstanding Paper award. As of October 2026, she is an associate professor in UBC Computer Science.

Timeline

  1. 2011

    A Swarthmore B.A. thesis

    At Swarthmore College's Computer Science Department, she writes a B.A. thesis, Integrating Human Knowledge into a Relational Learning System, in 2011.

  2. 2016

    A PhD at Carnegie Mellon

    At Carnegie Mellon's Computer Science Department, she completes a Ph.D. with Jeff Schneider in 2016 and writes Scalable, Flexible, and Active Learning on Distributions.

  3. 2016

    Optimized MMD for GANs

    In 2016, she coauthors an arXiv paper that optimizes a maximum mean discrepancy test and applies the method to generative adversarial networks.

  4. December 2020

    Joining UBC Computer Science

    Before UBC, she holds positions at the Toyota Technological Institute at Chicago and the Gatsby Computational Neuroscience Unit at University College London. In late December 2020, she moves to Vancouver and begins an assistant professorship in UBC Computer Science.

  5. 2021

    A Canada CIFAR AI Chair

    In 2021, she becomes a Canada CIFAR AI Chair through the Pan-Canadian AI Strategy and affiliates with Amii and UBC Computer Science.

  6. 2021

    POT for optimal transport

    In 2021, she coauthors POT, an open-source Python toolbox that implements optimal transport methods for machine learning.

  7. 2025

    An ICLR Outstanding Paper

    With Yi Ren, she studies how training examples shape predictions on other responses during LLM fine-tuning and offers an explanation for some hallucinations that strengthen after tuning. The paper describes the direct preference optimization (DPO) squeezing effect and wins an ICLR Outstanding Paper award in 2025.

  8. As of October 2026

    Associate Professor at UBC

    As of October 2026, she is an associate professor in UBC Computer Science.

Career

  1. Since 2020University of British ColumbiaAssistant Professor
  2. From 2021AmiiAffiliate

Education

  1. 2016Carnegie Mellon UniversityPh.D.
  2. 2011Swarthmore CollegeB.A.

Sources and credits

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  1. Danica J. Sutherland djsutherland.ml. Checked 21 October 2026.
  2. Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy arxiv.org. Checked 21 October 2026.
  3. New Assistant Professor blends machine learning and stats cs.ubc.ca. Checked 21 October 2026.
  4. Danica J. Sutherland — Alberta Machine Intelligence Institute amii.ca. Checked 21 October 2026.
  5. New talent at UBC grows B.C.’s AI research expertise cs.ubc.ca. Checked 21 October 2026.
  6. POT: Python Optimal Transport jmlr.org. Checked 21 October 2026.
  7. Learning Dynamics of LLM Finetuning proceedings.iclr.cc. Checked 21 October 2026.
  8. Announcing the Outstanding Paper Awards at ICLR 2025 blog.iclr.cc. Checked 21 October 2026.
  9. Danica Sutherland | Computer Science at UBC cs.ubc.ca. Checked 21 October 2026.