I am a Flatiron Research Fellow in the Center for Computational Neuroscience and a Visiting Scholar at NYU. I completed my Ph.D. in the Center for Theoretical Neuroscience at Columbia University primarily advised by Liam Paninski.

I work on statistical modeling of neural data and the overarching goal of my research is to causally understand biological and artificial recurrent neural networks and dynamical mechanisms therein. I’m interested in a wide range of topics including machine learning, statistics, causal inference, dynamical systems, neuroscience, and computer vision. In ML and statistics, I’ve worked on applied optimal transport, partial information decomposition, switching linear dynamical systems, causal dynamical systems, and covariance estimation. In computer vision, I’ve built automated tools for the segmentation, detection, and tracking of cells in microscopy images and videos. I’ve also developed statistical atlas construction methods for capturing structural variability across a population of animals. I’ve also extended non-negative matrix factorization (NMF) to deformable NMF and applied it to calcium demixing in non-stationary videos. In computational neuroscience, I’ve developed and applied functional and interventional connectivity estimation techniques. I’m currently working on representational similarity analysis for comparing neural representation across animals, species, and neural networks. Visit my Google Scholar for a list of my publications.

Apart from my academic life, I’m very much into sports and dancing. I play soccer in a NYC league and occasionally play basketball, swim, bike and run. I’m currently learning figure skating and I’m part of a Persian dance group called GISU.