We are accepting applications for PhD (and Master's) programs for Fall 2022 entry from both Domestic and International students!
Apply before Dec, 1st 2021 to UofT programs in Computer Science, Psychology or before Nov, 15th 2021 to Statistics. If you are interested in one of our research directions start preparing your application today!
We are looking for students with a variety of academic backgrounds, some examples are: HCI, Psychology, Mental Health, Machine Learning + Statistics. We welcome applications from groups, underrepresented in CS and Stats. 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. Students can apply through Computer Science, or through Psychology or Statistics (where I have cross-appointments and can supervise students). Our lab is highly interdisciplinary and currently includes students from HCI, Machine Learning, Psychology, Mental Health, Education, Economics, and Statistics – no one student is expected to have all these skills, they all teach other!
If you want to learn more about the lab culture, read and comment Value Statement http://tiny.cc/valuesiai2021 (request access). You can also get a sense of our lab culture by looking at these videos which some students kindly recorded for me on my birthday! Full 9 minute version and shorter prototype A and shorter prototype B some students created.
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, read one or two relevant Papers and email me with a summary or reflections & questions, or look at these four talks I've given to HCI, Psychology, Machine Learning, Statistics. You can choose whichever are most relevant to the area you want to work in: Talks Illustrating Examples of Lab Research. 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 Inzlicht), 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 program in Computer Science, or Psychology, or Statistics, and list me as a potential advisor. Note that the Master's is a research program available to Canadian Citizens or Permanent Residents (and the MScAC is a professional master's without a research component).
You can also send an email to iaiinterest@googlegroups.com 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.
## Talks Illustrating Examples of Lab Research## HCI (Human-Computer Interaction) targeted talk (UWashington DUB/HCI series)
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 ## Machine Learning targeted talk (Vector, MILA-McGill/Stanford)Vector Institute for Artificial Intelligence Jan 2021:
How can we transform the everyday technology people use into intelligent, self-improving systems? For example, how can we perpetually enhance text messages for managing stress, or personalize explanations in online courses? Our work explores the use of randomized adaptive experiments that test alternative actions (e.g. text messages, explanations), aiming to gain greater statistical confidence about the value of actions, in tandem with rapidly using this data to give better actions to future users. To help characterize the problems that arise in statistical analysis of data collected while trading off exploration and exploitation, we present a real-world case study of applying the multi-armed bandit algorithm TS (Thompson Sampling) to adaptive experiments. TS aims to assign people to actions in proportion to the probability those actions are optimal. We present empirical results on how the reliability of statistical analysis is impacted by Thompson Sampling, compared to a traditional experiment using uniform random assignment. This helps characterize a substantial problem to be solved – using a reward maximizing algorithm can cause substantial issues in statistical analysis of the data. More precisely, an adaptive algorithm can increase both false positives (believing actions have different effects when they do not) and false negatives (failing to detect differences between actions). We show how statistical analyses can be modified to take into account properties of the algorithm, but that these do not fully address the problem raised. We therefore introduce an algorithm which assigns a proportion of participants uniformly randomly and the remaining participants via Thompson sampling. The probability that a participant is assigned using Uniform Random (UR) allocation is set to the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained by exploiting. The resulting data can enable more accurate statistical inferences from hypothesis testing by detecting small effects when they exist (reducing false negatives), and reducing false positives. The work we present aims to surface the underappreciated complexity of using adaptive experimentation to both enable scientific/statistical discovery and help real-world users The current work takes a first step towards computationally characterizing some of the problems that arise, and what potential solutions might look like, in order to inform and invite multidisciplinary collaboration between researchers in machine learning, statistics, and the social-behavioral sciences. ## Alternative Talks/Previous VersionsTalk at MILA (Montreal Machine Learning & Artificial Intelligence) Dec 2019;
## Psychology targeted talk (Social-Personality & Cognitive Psych at U of T)Indiana University Psychology Talk Dec 2019
U of T Social Psychology Research Group talk Sep 2019:
## Statistics targeted talk (Cambridge, UMichigan, Columbia)Title:
Adapting Real-World Experimentation To Balance Enhancement of User Experiences with Statistically Robust Scientific DiscoveryCambridge University May 2021
To help characterize the problems that arise in statistical analysis of data collected while trading off exploration and exploitation, we present a real-world case study of applying the multi-armed bandit algorithm TS (Thompson Sampling) to adaptive experiments, providing more empirical context to issues raised by past work on adaptive clinical trials. TS aims to assign people to actions in proportion to the probability those actions are optimal. The empirical results help characterize how a reward maximizing algorithm can increase both false positives (believing actions have different effects when they do not) and false negatives (failing to detect differences between actions). We explore two methods that take into account properties of the TS algorithm used to collect data: inverse-probability weighting and an 'algorithm-induced hypothesis test' that uses non-parametric simulations under the null. These help but do not fully address the problems raised. We therefore introduce an algorithm which assigns a proportion of participants uniformly randomly and the remaining participants via Thompson sampling. The probability that a participant is assigned using Uniform Random (UR) allocation is set to the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained by exploiting. The resulting data can enable more accurate statistical inferences from hypothesis testing by detecting small effects when they exist (reducing false negatives), and reducing false positives. The work we present aims to surface the underappreciated complexity of using adaptive experimentation to both enable scientific/statistical discovery and help real-world users. We conduct field deployments and provide software the community can use to evaluate statistical tests and algorithms in complex real-world applications. This helps provide first steps towards integrating two key approaches: (1) How can we modify statistical tests to better match the properties of the algorithms that collect data in adaptive experiments? (2) How can we modify algorithms for adaptive experimentation to be more "statistically considerate" in being better suited to inference and analysis of data, while maximizing chances of giving participants useful arms? Tackling these questions requires multidisciplinary collaboration between researchers in machine learning, statistics, and the social-behavioral sciences. This is joint work with Nina Deliu, Sofia Villar, Audrey Durand, Anna Rafferty and others. ## 11 min TEDx Talk for broad audiencebit.ly/tedxwilliams If you liked what you see and are
ready to join:Optionally, write to iaiinterest@googlegroups.com with information about yourself, relevant research experience, what topics you're interested in and why, and why you want to pursue a PhD program. Look at Computer Science (for HCI, ML, CS Education), Psychology, or Statistics program descriptions and requirementsFollow Application Checklist & Process at School of Graduate Studies website |