I’m an Assistant Professor in the Neuroscience Department at Columbia University and Principal Investigator in the Center for Theoretical Neuroscience at the Zuckerman Mind Brain and Behavior Institute. My group works on topics including neural population dynamics, latent variable models and stochastic processes, as well as probabilistic unsupervised machine learning methods in general. Broadly, we use data analysis and theoretical modelling approaches to study neural computation through the lens of dynamical systems.

Before joining Columbia, I was a postdoc at Stanford University where I worked with Krishna Shenoy and Scott Linderman. I did my PhD work at the Gatsby Computational Neuroscience Unit at University College London under the supervision of Maneesh Sahani. I have an undergraduate degree in Natural Sciences and a master’s degree in Computational Statistics and Machine Learning, both from UCL. Before starting my PhD, I was a research assistant in Jonathan Pillow’s group at the Princeton Neuroscience Institute, where I developed scalable methods for Bayesian receptive field inference.

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