Thomas L. Griffiths has been at the heart of a set of ideas that have revolutionized cognitive science. He has used the Bayesian approach to provide deep, novel insights into core topics in cognitive psychology such as semantic memory, causal learning, similarity, and categorization. His work is distinctive in drawing on current work in machine learning, artificial intelligence, and statistics to provide new, formal tools for understanding human cognition.
Griffiths has also explored how Bayesian models of cognition might be applied in the context of cultural evolution. His research provides a formal account of how we might expect cultural universals to be derived from individual cognition — a fundamental question in anthropology and linguistics.
Griffiths also has made important advances in machine learning motivated by considering cognitive problems. In particular, his work on nonparametric Bayesian statistics resulted in the definition of a new class of stochastic processes and his algorithms for inference in topic models are widely used in the information retrieval community.
Griffiths work on probabilistic reasoning was featured in The Economist, and a paper on the reconstruction of ancient languages, published in the Proceedings of the National Academy of Sciences, generated an extended feature on BBC Radio, among other press. Griffiths is currently engaged in writing a book introducing key ideas from computational cognitive science to a popular audience.
Griffiths is director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at the University of California, Berkeley. He received his Ph.D. in psychology from Stanford University in 2005.