Scholar
Ruth Urner
Google Scholar ID: O7p7lRAAAAAJ
Associate Professor, York University, Toronto
Citations & Impact
Citations
675
H-index
16
i10-index
20
Publications
20
Co-authors
13
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Publications:
- - On the Computability of Robust PAC Learning, with Pascale Gourdeau and Tosca Lechner, accepted to COLT 2024.
- - Investigating Calibrated Classification Scores through the Lens of Interpretability, with Master's student Alireza Torabian, accepted to XAI 2024.
- Invited talks/tutorials/panel discussions:
- - Keynote speaker at Women in Data Science Regensburg Conference 2021, Regensburg, Germany, April 2021: Deciphering fairness desiderata for machine learning.
- - Keynote speaker at The Sixth International Conference on Machine Learning, Optimization, and Data Science (LOD), Sienna, Italy, July 2020: Promises and Challenges of Transfer Learning.
- - Keynote speaker at Women in Data Science Zurich Conference 2019, Zurich, Switzerland, April 2019: Invitation to thinking about Machine Learning.
- - Invited talk at Workshop on Pitfalls of limited data and computation for Trustworthy ML @ ICLR, Kigali, Rwanda/Zoom, May 2023: How (not) to Model an Adversary.
- - Tutorial at Dagstuhl Seminar, Beyond Adaptation: Understanding Distributional Changes, Schloss Dagstuhl/online, August 2020: An Overview on Domain Adaptation and Modelling of Dataset Shift (joint with Shai Ben-David).
- - Panelist at IEEE Toronto ComSoc: Moving the dial, Women in STEM Panel, Toronto/Zoom, July 2020.
- - Panelist at YorkU Inclusion Day 2020, York University, February 2020: Artificial Intelligence and Human Rights at YorkU: A Panel Discussion on Impacts and Opportunities.
- - Vignette at Fields/VISTA Mathematics of Vision Workshop 2019, Fields Institute, Toronto, October 2019: Vignette 10: Machine Learning.
- - Invited talk at Lorentz Center.
Research Experience
- International teaching activities/teaching at summer schools:
- - Hausdorff School on Algorithmic Data Analysis, Bonn, Germany, Course on Statistical Learning Theory, May 2022.
- - SMILES: Online Summer of Machine Learning at Skoltech 2020, Moscow, Russia/online, Course on Learning Theory, August 2020.
- - Pre-Doc Summer School on Learning Systems, ETH Zürich, Switzerland, Course and Tutorial on Learning Theory, July 2017.
- - Co-taught Machine Learning Theory with Ilya Tolstikhin, Tübingen University, Germany, Winter Semester 2016/17.
- - Machine Learning Summer School (MLSS), Tübingen, Germany, Tutorial for Learning Theory, July 2015 and June 2017.
- Teaching at York:
- - MATH 1090 Introduction to Logic for Computer Science (F18, F19, F22).
- - EECS 2001 Introduction to the Theory of Computation (W21, W23).
- - EECS 4404/5327 Introduction to Machine Learning and Pattern Recognition (W18, W19, SU19, F20, W22, F22).
- - EECS 6127 Machine Learning Theory (F17, W19, W20, F20, F21, F22).
- - EECS 6002 Machine Learning and Society (reading course) (SU19).
Background
- Develops mathematical tools and frameworks for analyzing the possibilities and limitations of automated learning, with a focus on semi-supervised, active, and transfer learning. Currently particularly interested in developing formal foundations for topics relating to societal impacts of machine learning, such as human interpretability and algorithmic fairness.
