Prospective Graduate Students

I invite applications for PhD/Master's research with me on topics that excite you and intersect with my research agenda. Most of my past publications focus on education and learning, but I have a substantive set of work on health behaviour and habit change (e.g. encouraging exercise, mental health), and on projects in areas as wide-ranging as behavioural economics (encouraging charitable donations) and workforce development.

As a graduate student, you would set the agenda for research questions in collaboration with me. Just to give examples, I'm particularly interested in creating systems that combine rigorous randomized experiments with crowdsourcing and human computation, applications of statistical machine learning (e.g. bandits & reinforcement learning, NLP, recommender systems), and theories from cognitive, clinical and social psychology (e.g. self-explanation, analogical comparison, growth mindset, teaching cognitive behaviour therapy).

To find out more about what I do, you can read my Research Statement or look at these talks: Slides for broad HCI audience, Recording for broad HCI audience (45 min), or an education-specific TEDx talk.

Based on alignment of interests and time, students may have opportunities to collaborate with people in U of T's Computer Science Education research group (e.g. Andrew Petersen), the Vector Institute/Artificial Intelligence/Machine Learning group (e.g. Amir Massoud Farahmand, Marzyeh Ghassemi), HCI people at DGP (e.g. Tovi Grossman, Fanny Chevalier), Psychology Department (e.g. Cendri Hutcherson, Mickey Inslicht), the Education School OISE, and many other areas like Computational Social Science (e.g. Ashton Anderson).

Graduate students will play a key role in deciding which projects are pursued, but illustrative examples of potential research directions are:
  • Developing new systems for crowdsourcing the design of online problems and lessons, using multi-stage workflows that incorporate input from students, crowd workers, instructors, and learning scientists.

  • Creating and evaluating tools that enable collaboration between instructors and researchers, such as co-design of interventions and personalized lessons, and coordinated analysis of data about learning outcomes for students with different characteristics.

  • Investigating why and when prompting students to explain text/video lectures promotes learning, and understanding the effect of multi-modal interfaces that incorporate writing, speaking, and video creation. Teaching metacognitive skills and self-regulated learning of study behaviours, taking a user-centred approach to designing social-psychological interventions for enhancing motivation such as Growth Mindset and Wise Feedback.

  • Enhancing student wellness and mental health by testing interventions for encouraging people to exercise, monitor stress, apply principles from Cognitive Behaviour Therapy to managing emotions. Investigating how to support online peer-to-peer interactions for having discussions around issues like managing anxiety or developing socio-emotional skills.

  • Interpretable and Interactive Machine Learning Systems for dynamically enhancing and personalizing instruction, especially from the perspective of combining human computation with techniques from multi-armed bandits/reinforcement learning, Bayesian optimization, applications of deep learning to natural language processing.
If you're interested, please apply for the Ph.D/Master's program in Computer Science and list me as a potential advisor. Note that the Master's is a research program (the MScAC is a professional master's). You might also be interested in working with Tovi Grossman.

You can also send an email to with information about yourself, what relevant research experience you have, what parts of my website you've looked at and what you found interesting about them, what topics you're interested in and why, and why you want to pursue a PhD program.