Intelligent Adaptive Interventions Lab & Joseph Jay Williams

The Intelligent Adaptive Interventions lab is directed by Joseph Jay Williams, an assistant professor in Computer Science at U of Toronto, with (courtesy) cross-appointments to accept PhD students in Psychology and Statistical sciences, and a faculty affiliate at the Vector Institute for Artificial Intelligence.

Our research agenda is to transform components of any real-world user interfaces into adaptive systems that are perpetually enhancing and personalizing interventions to help people. For example, we've published papers on technology for education, learning, mental health, by testing competing ideas about how to design components of online homework, apps, text messaging interventions, and other interface components. A distinctive focus is micro-experimentation 'in the wild', where we conduct and build tools for randomized A/B comparisons that can be used by practitioners and scientists to test design decisions and hypotheses about how to help people. Another focus is adaptive experimentation – applying and modifying machine learning algorithms and statistical tests for more rapidly yet reliably using data from experiments to change which interventions future participants receive. 

Slides and recordings of overview talks are at Talks Illustrating Examples of Lab Research, or TEDxPortofSpain talk

We encourage students adding to the diversity of our lab to apply for PhDs starting in Fall 2023, to share drafts of your applications by November 2022 by emailing  iaiinterest@googlegroups.com. You can apply to any/all of Computer Science, Psychology, and Statistical Sciences. More information is at www.intadaptint.org/prospectivestudents

More Information about Joseph
I was previously an Assistant Professor at the School of Computing (Information Systems & Analytics, and NUS HCI Lab) at National University of Singapore (NUS), and did Research Fellow positions at Harvard and Stanford, after receiving my PhD from UC Berkeley.

Dynamic Experimentation       

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].

These successful applications are enabled by my integrative approach: I use my cognitive science theories in deciding the target actions for experimentation (e.g. explanations, motivational messages) and the metrics to optimize (e.g. student ratings, response rates). To generate new actions I design crowdsourcing workflows, leveraging my human-computer interaction research. Data from experiments is analyzed using methods from Bayesian statistics, and algorithms from machine learning are used to turn data into dynamic enhancement and personalization of users' experiences. My self-improving systems are powered by combining human intelligence – in generating hypotheses that can be tested with data – with statistical machine learning – to automate rapid iteration and improvement.
My TEDxPortofSpain talk explains how I use this approach in education, using MOOClets to intelligently adapt explanations for how to solve math problems. 

You can contact me at: williams@cs.toronto.edu. If you are interested in joining my group, you can email iaiinterest@googlegroups.com.

Office: Directions at tiny.cc/joffice.

TEDxPortofSpain: Is the internet replacing teachers?

News & Updates

More about my research

In blending scientific research and applications I collaborate and consult with domain/topic experts, instructors, designers, and researchers from diverse disciplines like education, psychology, and computer science. I draw on theories and methodology from research I have done, as well as synthesizing findings from other scientists, work on behavior change, reviews of evidence-based best practices for teaching and learning, practical experience as a statistics tutor, evaluations of educational technology products & authoring tools for e-learning, experience as an ed-tech consultant and science & technology advisor, and my own active development of interactive learning and behavior change resources

I am currently an Assistant Professor in Computer Science at the University of Toronto (with a Graduate appointment in Psychology and Statistics). Previously I was an Assistant Professor in the School of Computing (Information Systems & Analytics, and NUS HCI Lab) at National University of Singapore (NUS). Prior to that I was a Research Fellow at Harvard's VPAL (Vice Provost for Advances in Learning) Research Group, and a member of the Intelligent Interactive Systems group led by Krzysztof Gajos in Computer Science. I have a courtesy appointment as a Research Scientist in Computer Science at Worcester Polytechnic Institute, where I am a co-PI with Neil Heffernan on an NSF Cyberinfrastructure grant. We use the ASSISTments K12 online math platform to crowdsource randomized controlled trials from the broader scientific community. 

I was previously a postdoc at Stanford University in the Graduate School of Education and Lytics Lab, working with the Office of the Vice Provost for Online Learning and Candace Thille's Open Learning Initiative. I received my PhD in Computational Cognitive Science from UC Berkeley's Psychology Department. I worked with Tania Lombrozo to investigate why prompting people to explain "why?" helps learning, and with Tom Griffiths on using Bayesian statistics and methods from machine learning to characterize learning, reasoning, and judgment.