Prospective Graduate Students: Psychology


We are accepting applications for PhD (and Master's) programs for Fall 2022 entry from both Domestic and International students!
Psychology and psychology-related background students can apply before Dec, 1st 2021 either to UofT program in Psychology and Computer Science (HCI).

While we appreciate it if you have some background in Computer Science, computational methods or HCI, it is not a barrier if you do not!
You will have a lot of opportunities to learn them (and, in turn, illuminate CS/ML/Stats students more about Psychology) after acceptance.

We welcome applications of students from underrepresented groups
Our current students will try to provide short feedback on the expressions of interest of students from such groups,
if sent before the deadline to iaiinterest@googlegroups.com. 

We focus on using adaptive field experiments in real-world environments, with applications like education and health, like improving digital lessons and homework, or building apps to help people manage stress, exercise more, or make better decisions. These can touch on a range of psychological questions/theories, often guided by students' interests, but below are examples we collaborate with other cognitive science and psychology researchers on.


Examples of work (not comprehensive):

  1. Cognitive Psychology or Computational Cog Sci focus: Understanding how people learn by generating explanations, and testing models/theories of what makes a good explanation, by conducting field experiments in online programming courses.

  2. Statistics & Machine Learning focus: Using reinforcement learning and multi-armed bandit algorithms to conduct adaptive field experiments, exploring methodological issues like how to design optimal experiments, in tandem with answering empirical questions. 

Broad psychology interest + applications to education, health, decision-making. Helping university students learn and manage stress better, by designing apps and activities that allow us to test theories of how to motivate people to work harder, engage in self-regulation and goal-setting, apply cognitive behavior therapy to emotion regulation, or change habitual behavior like increasing exercise.


You can learn more about our research by watching representative talks, targeted at Psychology and HCI audiences.


Psychology targeted talk (Social-Personality & Cognitive Psych at U of T)

Indiana University Psychology Talk Dec 2019 

Slides: IU CogSci v2.pptx Recording: Indiana Psychology Talk Dec 2019.mp4 . 

U of T Social Psychology Research Group talk Sep 2019: 

Slides: SPRG Talk v5.pptx and Recording: SPRG Talk (rough, unedited).mp4.

Title: Conducting Adaptive Field Experiments that Enhance and Personalize Education and Health Technology

Understanding people’s complex real-world thinking is a challenge for psychology, while human-computer interaction aims to build computational systems that can behave intelligently in the real-world. This talk presents a framework for redesigning the everyday websites people interact with to function as: (1) Micro-laboratories for psychological experimentation and data collection, (2) Intelligent adaptive agents that implement machine learning algorithms to dynamically discover how to optimize and personalize people’s learning and reasoning. I present an example of how this framework is used to embed randomized experiments into-real world online educational contexts – like learning to solve math problems– and machine learning used for automated experimentation. Explanations (and experimental conditions) are crowdsourced from teachers and scientists, and reinforcement learning algorithms for multi-armed bandits used in real-time to discover the best explanations for optimizing learning. Ongoing research examines tools for managing stress by applying approaches like cognitive behavior therapy, helping university students self-regulate and plan, and encouraging health behavior change through a collaboration with Goodlife to get people to go to the gym.

HCI (Human-Computer Interaction) targeted talk (UWashington DUB/HCI series)

Slides: tiny.cc/iaislides Recording: tiny.cc/iairecording

Google slides with otter transcription: https://docs.google.com/presentation/d/1m8XaqRXqX6DfZItmyqJbUcW1bPSv3Bspl2nJKTS1_wQ/edit#slide=id.g9685929bb8_2_1294 

Short Title: Enhancing and Personalizing Technology Through Dynamic Experimentation

Long Title: Perpetually Enhancing and Personalizing Technology for Learning & Health Behavior Change: Using Randomized A/B Experiments to integrate Human-Computer Interaction, Psychology, Crowdsourcing & Statistical Machine Learning

How can we transform the everyday technology people use into intelligent, self-improving systems? Our group applies statistical machine learning algorithms to analyze randomized A/B experiments and give the most effective conditions to future users. Ongoing work includes comparing different explanations for concepts in digital lessons/problems, getting people to exercise by testing motivational text messages, and discovering how to personalize micro-interventions to reduce stress and improve mental health. One example system crowdsourced explanations for how to solve math problems from students and teachers, and conducted an A/B experiment to identify which explanations other students rated as being helpful. We used algorithms for multi-armed bandits that analyze data in order to estimate the probability that each explanation is the best, and adaptively weight randomization to present better explanations to future learners (LAS 2016, CHI 2018). This generated explanations that helped learning as much as those of a real instructor. Ongoing work aims to personalize, by discovering which conditions are effective for subgroups of users. We use randomized A/B experiments in technology as an engine for practical improvement, in tandem with advancing research in HCI, psychological theory, statistics, and machine learning