معرفی
Zeinab Abboud is a Senior Academic Member and PhD researcher at Polytechnique Montréal affiliated with Mila (Québec Artificial Intelligence Institute). Her work focuses on Bayesian neural networks, computer vision, and AI applications in healthcare, particularly medical image analysis.
- PhD in Artificial Intelligence from Polytechnique Montréal
- Supervised by Hervé Lombaert
Her research addresses computational efficiency in medical image analysis through sparse Bayesian networks, combining deterministic and Bayesian parameter representations to reduce training complexity while maintaining predictive accuracy. She has contributed to multi-label classification (ChestMNIST) and segmentation tasks (ISIC, LIDC-IDRI) in healthcare imaging.
The publication Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis (2024) demonstrates her work on reducing Bayesian parameter usage by 95% compared to traditional methods, advancing scalable uncertainty estimation in AI models for medical applications.
