About

I earned my PhD from NYU’s Center for Data Science, where I was advised by Yann LeCun. My research focuses on self-supervised learning methods for extracting meaningful data representations. In particular, I develop regularization techniques that prevent collapse during model training, ensuring that the learned representations remain informative and useful.

Beyond my doctoral work, I am excited about advancing modern AI, including large language models and multi-modal generative systems. Leveraging my research experience, I aim to evaluate and build more robust and scalable AI systems that generalize effectively across diverse tasks.