Many fields and industries are witnessing huge increases in the quantity and complexity of recorded data. This changing data paradigm will only lead to a similarly dramatic increase in theoretical understanding and useful technologies if we create the analytical methods to meaningfully interrogate this data. Creating and applying these statistical and machine learning algorithms is the focus of my research. My most frequent application of these methods is to neural data: we use our brain in everything that we do, but we understand relatively little about how it works at a computational level. For example, how do populations of neurons control complex, sophisticated movement, and how can we design biomedical technologies based on these scientific insights?

I received a B.A. in computer science from Dartmouth (USA) in 2002, M.S. and Ph.D. degrees in electrical engineering from Stanford (USA) in 2006 and 2009, after which I did postdoctoral work in the Machine Learning Group at Cambridge (UK), where I was the Sackler Engineering Fellow of Christ’s College. I am presently an associate professor at Columbia University in the Department of Statistics, where I am also a member of the Grossman Center for Statistics of Mind, and a member of the Neurobiology and Behavior Graduate Program.

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