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.