I’m a post-doctoral fellow at CSAIL MIT. Prior to that I was a Flatiron Research Fellow at the Simons Foundation, working jointly at the Center for Computational Neuroscience and the Center for Computational Mathematics. I got my Ph.D. in Data Science in 2024 at the Center for Data Science at New York University, advised by Eero Simoncelli. Here is my thesis. I studied Solid State Physics for my bachelor’s and Psychology for my master’s.

I’m broadly interested in vision and more specifically in probability densities of natural images. I have studied these densities from various angles: learning them from data, understanding and evaluating the learned models, and utilizing them for real-world problems. These areas are closely intertwined: understanding a learned model can inspire the design of better and more efficient ones. Conversely, better performance can hint at something meaningful the model has captured about the underlying data structures. I enjoy studying these complementary perspectives and seeing how they inform one another through careful and controlled scientific experimentation.

Read more about my work on the Research page.