I am an MS by Research student in Artificial Intelligence and Data Science with a strong foundation in applied machine learning and deep learning. My work focuses on understanding models beyond surface-level performance, emphasizing objective design, optimization behavior, and evaluation of model reliability.

I have hands-on experience with vision models and ML systems, and a solid theoretical grounding in generative modeling, including loss formulations, KL-divergence, and ELBO-based objectives. I am particularly interested in how optimization principles influence generalization, robustness, and reasoning in modern learning systems, and I aim to transition this understanding toward multimodal and language-based models.