About Me

My research agenda 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.

I am an Assistant Professor in the School of Computing (Information Systems & Analytics, and NUS HCI Lab) at National University of Singapore (NUS), where I moved after Research Fellow positions in digital education groups at Harvard and Stanford, and receiving my PhD in Computational Cognitive Science 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@comp.nus.edu.sg. 

Office: COM2 04-19, 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 the School of Computing (Information Systems & Analytics, and NUS HCI Lab) at National University of Singapore (NUS). Previously 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.