AI De-biasing & Fairness in Computer Vision
Academic Projects @ York University#
In early 2026, I focused on the theoretical and practical aspects of machine learning fairness.
Key Areas:#
- De-biasing: Analyzed methods to mitigate bias in AI models, specifically for Image Upsampling tasks.
- Theory: Studied PAC learnability and VC dimension to understand the limits of what machines can learn.
- Image Processing: Implemented Gaussian noise injection and restoration algorithms.