Name:Joseph Jay Williams

Bio:

Joseph Jay Williams is an Assistant Professor in Computer Science at the University of Toronto. He has courtesy appointments in Statistical Sciences & Psychology (to accept & supervise PhD students), as well as Economics and Mechanical & Industrial Engineering, and is a Vector Institute for Artificial Intelligence Faculty Affiliate. He leads the Intelligent Adaptive Interventions research group of 11 graduate students across HCI (Human-Computer Interaction), Psychology, applied ML, & Statistics. Our goal is to reimagine the use of randomized experiments to turn any user interface into an intelligent, perpetually improving system. Joseph was previously an Assistant Professor at the National University of Singapore’s School of Computing in the department of Information Systems & Analytics, a Research Fellow at Harvard’s Office of the Vice Provost for Advances in Learning, and a member of the Intelligent Interactive Systems Group in Computer Science. He completed a postdoc at Stanford University in Summer 2014, working with the Office of the Vice Provost for Online Learning and the Open Learning Initiative. He received his PhD from UC Berkeley in Computational Cognitive Science (with Tom Griffiths and Tania Lombrozo), where he applied Bayesian statistics and machine learning to model how people learn and reason, and used randomized experiments to develop theories of how people learn by explaining to themselves and others. He received his B.Sc. from University of Toronto in Cognitive Science, Artificial Intelligence and Mathematics, and is originally from Trinidad and Tobago. More information about the Intelligent Adaptive Intervention group’s research and papers is at www.intadaptint.org.

Country:Canada

Affiliation:University of Toronto

Research interests:A/B Testing, Experiment Inspired Design, Computer Science & Education, Human-Computer Interaction, Machine Learning/Artficial Intelligence focused on NLP and Reinforcment Learning