Joshua B. Tenenbaum

Tenenbaum is a scientist who has helped to create a new school of thought in cognitive science – the approach involving Bayesian inference of probabilistic models. This school, in which Tenenbaum has synthesized key perspectives from machine learning and Bayesian statistical inference, is providing fundamental new insights into the deepest questions humans have ever asked themselves: the origins of human knowledge and the nature of human cognition. Tenenbaum has applied insights from Bayesian statistical inference to solve some of the classic problems in cognitive science: How do we discover the causal structure of the world, or predict the future, from the sparse data sets derived form of our individual experience? How do children determine the meaning of words (from the infinite set of hypotheses consistent with the available data)? How do we discover structure in noisy data sets, without having to assume some framework in advance? How can we formalize everyday physics and psychology in a way that will allow us to make the right inferences about future events? Tenenbaum is an exceptionally creative scientist who has combined machine learning, Bayesian inference, and cognitive science to produce original and influential insights about the nature of the human mind.