My primary research interest is self-supervised machine learning, based on biologically inspired models of learning in animals. Reciprocally, I'm also interested in computational models of learning in biological systems. My research is motivated by the observation that animals routinely solve extremely difficult, nonparametric learning problems during development. The goal of my work is to create more sophisticated computational systems by understanding how animals do this.
Toward this end, I particularly love the study of clustering, the structure of inductive bias, and computational analysis of complex biological data.
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