I am Joe Austerweil, an Assistant Professor at Brown University in the Department of Cognitive, Linguistic, and Psychological Sciences.
As a computational cognitive psychologist, my research program
explores questions at the intersection of perception and higher-level
cognition. I use recent advances in statistics and computer science to
formulate ideal learner models to see how they solve these problems
and then test the model predictions using traditional behavioral
experimentation. Ideal learner models help us understand the knowledge people use to solve problems because such knowledge must be made explicit for the ideal learner model to successfully produce human behavior. This method yields novel machine learning methods and
leads to the discovery of new psychological principles.
Brown University (2007), Sc. B. in Applied Mathematics-Computer
Science (with honors)
University of California, Berkeley (2011), M.A. in Statistics
University of California, Berkeley (2012), Ph.D. in Psychology
I am organizing a symposium on Computational Constructivism at the Eastern Psychological Association in Boston, MA, March 13-16, 2014.
I am teaching Thinking (CLPS 1200) Fall 2013. Here is its syllabus [PDF].
I will be teaching Human and Machine Learning (CLPS 1211) Spring 2014. Here is its syllabus [PDF].
I am interested in taking PhD students for starting graduate school Fall 2014. Please send me an email if you are interested.
Joseph Austerweil and Thomas Griffiths. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120 (4), 817-851. [DOI]
Last Updated November 20, 2013