Greater Toronto Area, Canada
I'm a professor in HCI, Applied AI (LLMs), Applied ML, psychology, statistics, economics and industrial engineering. My Intelligent Adaptive Interventions lab uses technology to help people change behaviour. We won an xprize for our A/B 'SuperExperimentation' infrastructure being the future of experimentation in education (http://tiny.cc/ixprize). My research agenda (see more at http://tiny.cc/williamsresearch) is to create intelligent self-improving systems that conduct dynamic experiments to discover how to optimize and personalize technology, helping people learn new concepts and change habitual behavior. This requires using computational cognitive science and Bayesian statistics to bridge human-computer interaction with machine learning, with applications to education and health behavior change. Papers across all these disciplines are at www.josephjaywilliams.com/papers. I'm open to consulting work to: (1) Help design randomized field experiments or A/B testing, especially applied to education, training and health behavior change. (2) Applying statistics and machine learning to do A/B testing automatically, using algorithms like multi-armed bandits to enhance and personalize which conditions future users receive. (3) Advising on the development of infrastructure for A/B testing that enables the use of machine learning, personalization, and crowdsourcing. I am an Assistant Professor in the Department of Computer Science at University of Toronto. I moved here after an Assistant Professor position at National University of Singapore, Research Fellow positions in digital education groups at Harvard and Stanford, and receiving my PhD in Computational Cognitive Science from UC Berkeley. One example of my research is a system I created for automatically experimenting with explanations, which enhanced learning from math problems as much as an expert instructor [LAS 2016]. Another system boosted people's responses to an email campaign, by dynamically discovering how to personalize motivational messages to a user's activity level [EDM 2015]. Specialties: Online Education, E-learning, Learning, Cognitive Science, Research, Survey Software, E-learning software, Explanation, Experiments,
Research on evaluating and improving how people use AI - using new Adaptive Experimentation methods.
Assistant Professor in the School of Computing, department of Information Systems & Analytics. Research on intelligent technologies for learning and health behavior change, driven by crowdsourced, dynamic, personalized experimentation and statistical machine learning.
Research Fellow at VPAL (Vice Provost for Advances in Learning) Research Group: Human-Computer Interaction and Cognitive Science research on improving engagement and reflection by experimenting and personalizing people's interactions with digital online educational resources like MOOClets.