Ruth Urner, York University

Research

Fairness in Machine Learning Interpretability in Machine Learning Safety in Machine Learning Statistical guarantees for learning algorithms Theory of Computer Science Theory of Machine Learning Transfer learning

Latest funding

Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning
NSERC · 2018 · Principal investigator
$231,000
Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning
NSERC · 2018 · Principal investigator
$12,500

All 2 grants and awards

Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning

Principal investigatorNSERC2018Discovery Grants Program - Individual

Principal investigators: Urner, Ruth

Keywords: Fairness in Machine Learning; Interpretability in Machine Learning; Safety in Machine Learning; Statistical guarantees for learning algorithms; Theory of Computer Science; Theory of Machine Learning; Transfer learning

$231,000

Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning

Principal investigatorNSERC2018Discovery Launch Supplement

Principal investigators: Urner, Ruth

$12,500

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

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Applying for

Subject: Prospective PhD student: Fairness in Machine Learning Dear Professor Urner, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. Your research on fairness in machine learning connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]