Scholar
David Madras
Google Scholar ID: MgnNDpkAAAAJ
University of Toronto
Citations & Impact
Citations
11,445
H-index
14
i10-index
15
Publications
20
Co-authors
13
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Paper 'Causal Modelling for Fairness in Dynamical Systems' accepted to ICML 2020; 'Detecting Extrapolation with Local Ensembles' accepted to ICLR 2020; 'Flexibly Fair Representation Learning by Disentanglement' accepted to ICML 2019; 'Fairness Through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data' accepted to FAT* 2019 and presented at the Workshop on Ethical, Social, and Governance Issues in AI.
Research Experience
- Worked as a research intern at Google Brain under the supervision of Alex D'Amour, focusing on causal inference and out-of-distribution detection; co-organized and served as program chair for the inaugural Pan-Canadian Self-Organizing Conference on Machine Learning (PC-SOCMLx); attended the NBER Economics of Artificial Intelligence Conference and the pre-conference NBER Economics of AI Young Scholars Workshop; taught a course on Privacy and Fairness in Machine Learning at the African Master's in Machine Intelligence in Kigali, Rwanda; gave a talk at Princeton University.
Education
- PhD Student in the Machine Learning Group at the University of Toronto, supervised by Rich Zemel.
Background
- PhD Student in the Machine Learning Group at the University of Toronto, supervised by Rich Zemel. Primarily interested in learning better and fairer algorithmic decision-making systems, with a focus on fairness, causal inference, and generative modelling.
Miscellany
- Participated in teaching activities at the African Institute for Mathematical Sciences; interested in interdisciplinary exchanges, attended multiple international conferences and workshops.
