Working at Mila
Being surrounded by hundreds of AI researchers at Mila creates an exceptional environment to develop ideas and make new friends!
What I like working on
I like working on responsible AI-related topics, such as developing novel and efficient social bias mitigation methods and studying harms like hate speech and hallucination. Given the limitations of bias metrics, I am also interested in introducing better fairness assessment techniques.
Publications
- Should we attend more or less? modulating attention for fairness
- [arXiv]
- Why Don’t Prompt-Based Fairness Metrics Correlate?
- [arXiv] [slides] [video] [longer video]
- Fairness-Aware Structured Pruning in Transformers
- [arXiv] [slides] [video]
- Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
- [arXiv] [slides] [video] [blog]
- Ultrasound Elastography using Machine Learning A. Zayed
- [Link]
- Fast Strain Estimation and Frame Selection in Ultrasound Elastography using Machine Learning A. Zayed and H. Rivaz
- [Link] [code]
- Automatic Frame Selection using CNN in Ultrasound Elastography A. Zayed, G. Cloutier and H. Rivaz
- [arXiv]
- Rapid Quantification of White Matter Disconnection in the Human Brain A. Zayed, Y. Iturria-Medina, A. Villringer, B. Sehm and C. Steele
- [arXiv]
- Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography A. Zayed and H. Rivaz
- [arXiv]
- Fast approximate time-delay estimation in ultrasound elastography using principal component analysis A. Zayed and H. Rivaz
- [arXiv]
1st Conference on Language Modeling (COLM 2024)
TL;DR: We show that bias can be mitigated by focusing on a wider/narrower context.
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)
TL;DR: We explain why fairness metrics don't correlate and propose CAIRO to make them correlate.
The thirty-eighth Association for the Advancement of Artificial Intelligence (AAAI 2024)
TL;DR: We show that certain attention heads are responsible for bias and pruning them improves fairness.
The thirty-seventh Association for the Advancement of Artificial Intelligence (AAAI 2023)
TL;DR: We quantify the contribution of each training example to fairness, and choose the best training examples to reduce bias.
Master's Thesis, 2020
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC), 2020
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2020
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2020
International Conference on Image Analysis and Recognition (ICIAR), 2019
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2019