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
MIT in Late April-Early May, 2015. More to come soon.
I will be teaching Introduction to Human Cognition (CLPS 0200) in Winter 2015. Syllabus to come soon.
I am interested in taking PhD students for starting graduate school Fall 2015. Please send me an email if you are interested.
Joshua Abbott, Joseph Austerweil, and Thomas Griffiths (in press). Random walks on semantic networks can resemble optimal foraging. Psychological Review. [Preprint PDF]
Joseph Austerweil and Thomas Griffiths. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120 (4), 817-851. [DOI]
My laboratory is developing open source tools to perform fast (GPU-based using OpenCL) and easy (written for use in Python) inference for Bayesian nonparametric models. The current methods are available at Github. This effort is spearheaded by a postdoctoral scholar in my laboratory, Ting Qian.
Last Updated December 15, 2014