Contextual Bandits

Contextual bandit algorithms are powerful reinforcement learning techniques that enable personalization and user-focused design. They balance the exploration of new possibilities with the exploitation of the best existing options to learn and act optimally. Our lab conducts research on contextual bandits and their applications to various areas of human-computer interaction, such as encouraging people to exercise (where we use them to balance showing people new motivational messages with showing them those that have been proven to be effective in the past).

Mental Health

Mental health is an area where personalization and user-focused design can be especially beneficial for people. Likewise, there are two goals that we are trying to achieve with the mental health projects that we are working on. First, we are working on using machine learning to deploy personalized stress management interventions for students to help them handle, or cope with, stress. Secondly, we are working on deploying and measuring the effects of TenQ, a condensed ten-question survey informed by cognitive behavioural therapy meant to help people reflect on their mental health, with the goal of lowering the barrier to accessing mental health resources.


The DIAMANTE project focuses on the intersection of physical and mental health in patients with diabetes and depression. The project takes the form of an app in which participants keep track of a target number of steps walked daily and fill out a survey. We then apply contextual bandits to determine which factors are effective in helping users reach their step goals and improve their mental health. The app sends both motivational messages and requests for feedback to the users. For the first six days, it also sends one message per day with advice on managing diabetes and depression and asks the participant to rate their mood


We see great potential in the application of intelligent adaptive interventions to helping people stick to their exercise schedules. We are working with Goodlife Fitness to apply findings from the psychology of motivation and self-control in an automated text messaging system, specifically through the use of reinforcement learning to send participants automated motivational text messages to encourage them to go to the gym and help them meet their exercise goals.

Personalized Explanations

There is a large body of academic literature showing that students learn better when they write explanations of the concepts they are learning. This is great in a lab setting when we can ensure that students who participate in a study take the time to write out an explanation, but how can we introduce self-explanation into courses? In the personalized explanations project, we design randomized experiments to determine how to get students to write self-explanations in online learning systems.