I build computational models of everyday cognitive abilities, focusing on problems that are easier for people than they are for machines. The human mind is the best known solution to a diverse array of difficult computational problems: learning new concepts, learning new tasks, understanding scenes, learning language, asking questions, forming explanations, amongst many others. Machines also struggle to simulate other facets of human intelligence, including creativity, curiosity, self-assessment, and commonsense reasoning.
In this broad space of computational challenges, my work has addressed a range of questions: How do people learn a new concept from just one or a few examples? How do people act creatively when designing new concepts? How do people learn qualitatively different forms of structure? How do people ask questions when searching for information?
By studying these distinctively human endeavors, there is potential to advance both cognitive science and machine learning. In cognitive science, building a computational model is a test of understanding; if people outperform all existing algorithms on certain types of problems, we have more to understand about how people solve them. In machine learning, these cognitive abilities are both important open problems as well as opportunities to reverse engineer the human solutions. By studying the human solutions to difficult computational problems, I aim to better understand humans and to build machines that learn in more powerful and more human-like ways.
Please see my main webpage http://cims.nyu.edu/~brenden/ for more information.
EducationPh.D., Massachusetts Institute of Technology, 2014 (Cognitive Science)
M.S., Stanford University, 2009 (Symbolic Systems)
B.S., Stanford University, 2009 (Symbolic Systems)
PositionsAssistant Professor of Psychology and Data Science, New York University, 2017-
Moore-Sloan Data Science Fellow, New York University, 2014-2017
Please see my main webpage http://cims.nyu.edu/~brenden/ for a full list of publications.