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 these statistical and machine learning algorithms is the focus of our research.
We particularly focus on statistics for understanding computation in neural systems: 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?
The purpose of these algorithms is to advance scientific understanding of the neural basis of movement, and to advance computational learning methods in their own right. Our tools are drawn from statistics, computer science, and engineering, including: dynamical systems, dimensionality reduction, nonparametric statistics, approximate inference, optimization, and numerical linear algebra.
Member, Grossman Center for the Statistics of Mind
Member, Center for Theoretical Neuroscience
Member, Institute for Data Science and Engineering
Member, Neurobiology and Behavior Graduate Program