I am a deep learning researcher studying how inductive biases and physical signal formation shape representation learning in computer vision.
My research focuses on the design of convolutional and hybrid neural architectures that improve robustness and generalisation under complex, real‑world distribution shift. Throughout this work, I treat application domains as diagnostic tools for analysing model failure modes and representation stability, grounding architectural design in principles from visual perception and physical image formation.
