Joseph's Intelligent Adaptive Interventions Lab explores:
How can you turn any user interface into an intelligent, perpetually improving system? Innovations in A/B Experimentation and interfaces to LLMs.
This question guides our lab's use of Adaptive Experimentation to design Intelligent Interventions for Behaviour Change: How do we help people start doing things they want to, and stop doing things they don't?
One example of our work is to transform ubiquitous explanations and prompting questions (e.g. text in a webpage, email, SMS) into an Intelligent Adaptive Intervention. We do this by innovating in uses of A/B experimentation, like inventing the AdapComp toolkit that enhances & personalizes explanations/prompts by A/B testing alternative versions that are generated by human & artificial intelligence (coordinating contributions from designers, social-behavioural scientists, users, chatGPT-like systems), and using adaptive experiments that automatically analyze data and use it to give better versions to future users. Our adaptive experiments apply and advance machine learning algorithms and statistical tests.
Learn more at this Research Statement: tiny.cc/williamsresearch.
Our papers (www.intadaptint.org/papers) span publications in applied Artificial Intelligence & Machine Learning, HCI (Human Computer Interaction), Statistics, Cognitive/Social/Clinical Psychology, Digital Education, Mental Health, and other areas.
Joseph Jay Williams is the Director of the Intelligent Adaptive Interventions lab. He is an Assistant Professor in Computer Science (6 Grad Students), with courtesy appointments in Psychology (2 Grad Students) and Statistics (2 Grad Students), as well as Industrial Engineering, Economics, and the Vector Institute for Artificial Intelligence. More information on lab members is at www.intadaptint.org/people, about Joseph at his CV (tiny.cc/jjwcv), and you can watch recordings of talks for difference audiences (tiny.cc/williamstalks).
You can follow Joseph on LinkedIn, Facebook, Instagram, TikTok or any other social media.
Latest News!
Joseph announced on Monday he was giving a talk Fri 2pm on "Nobel-Prize-Level Research on Adaptive Experimentation That Can Help People Practically and Advance Scientific Experimentation: Statistically Sensitive Algorithms and Algorithm Attuned Analyses" [get details & zoom link at https://calendar.app.google/G2kAVp5kuK5xrRmy7].
Tuesday Geoff Hinton won a Nobel Prize for his Artificial Intelligence work! Joseph took Geoff's class in 2006 (as a UofT undergrad), and followed Geoff's suggestion to do his PhD at UC Berkeley, exploring AI & Human Computer Interaction/Psychology. Joseph's lab's research on using AI & HCI & Psych to help people was impacted by Geoff.
We've published papers on technology for education, learning, and mental health, by testing competing ideas about how to design components of online homework, apps, text messaging interventions, and other interface components.
Joseph's TEDxPortofSpain talk explains how we use this approach in education, using MOOClets to intelligently adapt explanations for how to solve math problems.
For more information, check out our Lab Vision page.
News
#Recent Accepted Publications
Two papers accepted to CHI' 24 on LLM Application, see them at www.intadaptint.org/papers!
#Highlights
We won the prestigious Xprize for transforming education through Perpetual-Experimentation. Read more at tiny.cc/ixprize and the paper tiny.cc/moocletpaper.
#Awards & ShoutOuts
Our QuickTA project combining AI (RL algorithms & #LLMs) with @TutorGen is one of 16 #ToolsCompetition finalists for the DARPA AI Tools for Adult Learning opportunity!
#Research Projects
We received a DSI (Data Science Institute) grant. Part of this is for crowdsourced interventions! We are working with 10+ researchers to bring their well-being intervention to life!
We gratefully acknowledge support from our sponsors:
Contact Us
Undergraduates & interested graduate students/postdocs interested in joining or collaborating, contact: iaiinterest@googlegroups.com.
Joseph can be contacted at
williams[at]cs[dot]toronto[dot]edu.