Joseph Jay Williams is an Assistant Professor in Computer Science at the University of Toronto (with Graduate Appointments in Psychology and Statistical Science), leading the Intelligent Adaptive Interventions research group. He was previously an Assistant Professor at the National University of Singapore's School of Computing in the Department of Information Systems & Analytics, a Research Fellow at Harvard's Office of the Vice Provost for Advances in Learning, and a member of the Intelligent Interactive Systems Group in Computer Science. He completed a postdoc at Stanford University in Summer 2014, working with the Office of the Vice Provost for Online Learning and the Open Learning Initiative. He received his PhD from UC Berkeley in Computational Cognitive Science, where he applied Bayesian statistics and machine learning to model how people learn and reason. He received his B.Sc. from the University of Toronto in Cognitive Science, Artificial Intelligence and Mathematics, and is originally from Trinidad and Tobago.

Associate Director

Michael is an Assistant Professor, Teaching Stream in the Department of Mathematical and Computational Sciences at the University of Toronto Mississauga (UTM). He is a computer science educator who prides himself in fostering a welcoming environment of educational excellence through active and experiential learning, as well as, through the use of behavioural intervention strategies. Michael has received the University of Toronto's Student Life Award for ​Outstanding Faculty Guidance & Support.

Regular Internal Faculty Collaborators

Andrew is a Professor, Teaching Stream in the Department of Mathematical and Computational Sciences (with a cross-appointment in the Institute for the Study of University Pedagogy) at the University of Toronto Mississauga (UTM). He takes special pride in his role as an educator and has received the University of Toronto’s President’s Teaching Award and the OCUFA Teaching Award. Andrew led the development of an online exercise system used in CS courses at U of T that provides a platform for exploration of student learning, and he also uses mixed methods to investigate barriers to student success in computing programs.

Graduate Students

I am Ananya Bhattacharjee, currently a 3rd year Computer Science Ph.D. student at University of Toronto. I completed B.Sc. in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET) in October 2018. 

I consider myself a Human-Computer Interaction Researcher. My research works focus on developing and understanding the role of technology in helping people manage their psychological well-being. I have developed several text messaging services, mobile applications, and websites to help people reduce their stress, reflect on negative emotions, and self-monitor moods. Collaborating with Mental Health America, I have closely worked with vulnerable population to understand their needs and expectations from digital mental health platforms, eventually allowing us to develop an AI-powered chat tool.

Additionally, I have conducted field studies in Bangladesh to understand how much of the existing support behavior techniques align with local people's customs and values. Working closely with a major crisis helpline in Bangladesh (Kaan Pete Roi), I have interacted with helpline volunteers and critically evaluated befriending model, one of the most common approaches followed by helplines around the world. Through my work, I aim to shift the research direction in mental health interventions from ‘individual’ to ‘social’.

My research works have been published in top venues like CHI, CSCW, TOCHI, and JMIR. My works have got Best Paper and Honorable Mention awards in CHI.


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Angela is a second-year Ph.D. student and course instructor in the Department of Computer Science (CS). Angela’s research focuses on using HCI methodologies in CS education to design better interventions and understand the type of support our students' needs. Some of the most recent projects she is involved in include investigating novel techniques to increase content retention, performance, and belonging, such as through voice self-explanations and email reminders. Anglea is also interested in learning about students’ experiences and struggles, including the prevalence of the Impostor Phenomenon in CS students.

Fred-Haochen Song

I'm Fred Haochen Song, a 1st-year Ph.D. student dedicated to exploring the intersection of statistical science and adaptive learning algorithms. In my current research, I focus on developing innovative tools to measure mental health concerns in large student populations. Simultaneously, I'm working on projects aiming to optimize resource allocation through dynamic statistical methodologies. One of these involves a novel integration of Thompson Sampling and Uniform Random Sampling, designed to balance trial efficacy with rewards. Another uses allocation probability in Thompson Sampling as a benchmark to effectively identify prior differences between groups. Leveraging these techniques, I aspire to address real-world challenges in education, mental health, and cognitive science. 


I am a 2nd year PhD student working at the intersection of Human-Computer Interaction, Reinforcement Learning and behavior change for good. My primary research focus is on using reinforcement learning algorithms (like contextual bandits) to optimize the interaction of users with technology. As part of Microsoft's AI for Accessibility grant, I have been working on an adaptive text-messaging system to personalize the content and delivery of text messages for the mental well-being of users, based on the contextual information of the users. Another line of work focuses on how pre-trained general AI models could augment user workflows. I aim to bridge my research in adaptive experiments with pre-trained language model research to explore the design space of self-improving and personalized conversational agents to improve the everyday lives of users. I will be interning in the Computational Social Science group at Microsoft Research this summer. 

I'm a 1st year PhD student. My research interests are in developing tools and approaches to support the theory-driven and instructor-led design of instructional, motivational and self-regulation adaptive interventions in education. More broadly I am interested in HCI, EdTech, Computer/Data Science Education (especially for non-STEM majors), Learning Engineering, Design and Analytics and Computational Social Science. Previously I was a Senior Lecturer and Jr Research fellow at HSE University St.Petersburg where I co-designed and taught courses and programs in Data Science, Computational Social Science and HCI, targeted primarily to non-STEM students.

I am a Ph.D. student in Computer Science at the DGP lab, and an affiliate member of UBC Language Sciences. My current research is focused on transforming digital learning platforms, such as online courses, into intelligent, continually improving systems using adaptive experimentation and iterative design. I earned my M.Sc. in Human-Computer Interaction from the University of British Columbia (‘20) and my B.Sc. in Computer Science and Economics from BRAC University (‘17), where I was awarded the Vice Chancellor's Gold Medal for ranking first in the CS program.

The easiest way to reach me is to email mohireza [at] []

Here’s a link to my website:, LinkedIn, and a list of some of my papers:

Dana Kulzhabayeva

I am a second-year Ph.D. student in Psychology, focusing on people’s causal attributions for successes and failures in daily goal pursuit and their impact on subsequent actions, belief-updating, and self-efficacy over time. I enjoy delving into theoretical psychology, I care about theoretical research with real-world impact. I aim to use my work to design interventions that can help individuals adaptively interpret negative feedback and make better decisions. Outside of research, I delight in the art of dancing, reading novels, and critiquing subpar Netflix originals with my younger siblings.

You can reach me on LinkedIn or at

Tong Li

Third-year PhD student in Statistics (started my Ph.D. in Sept 2021). I'm currently doing research on Bandit algorithms for randomized experiments, with the hope of understanding the performance of traditional Bandit algorithms such as Thompson Sampling and developing new algorithms that can achieve a better tradeoff between regret and statistical inference. I'm also in the Mental Health America project where we use Bandit algorithms to find text messages that can better help people with mental health issues.


Pan Chen is a first-year Ph.D. student in Computer Science who is interested in the intersection of computer science and data science. His research focuses on improving user interfaces through an adaptive experiment engine that uses artificial intelligence to personalize interventions to assist people. 

Over the past year, he has developed tools to deploy course content with adaptive content at OLI, contributing to the lab's top 3 position in the XPrize Digital Learning Challenge. He is currently working on democratizing the adaptive experiment tool to make it more accessible for anyone to conduct their experiments. 

Pan Chen is also exploring new ways to help students with their online homework, such as generating code summarizations using LLMs and providing an option for students to complete their assignments by speaking. He plans to conduct adaptive experiments himself to determine the effectiveness of these innovations in assisting people.

As a Computer Science Master's student, I investigate the dynamics of students using online learning tools, specifically Q&A forums. My projects focus on understanding student behaviors, exploring mindset reframing, and developing reflection systems. I previously studied Computer Science as an undergraduate at the University of Toronto Mississauga, where I gained firsthand insight into the positive impact of community recognition. My research investigates barriers to representation in online spaces, particularly for students seeking help and for students who need representation/role models. By exploring ways to reframe student mindsets and set expectations, I hope to find new ways to support computing programs and their students. 

During my undergraduate studies, I was fortunate enough to receive support from both the NSERC USRA and MCSRA awards, which allowed me to pursue research in my field. As a graduate student, I am grateful for the QEII-GSST award, which has been instrumental in supporting my current research. 

Outside of work, I enjoy reading books about aliens pretending to be human, indulging in Chinese cuisine, creating memes, and having deep conversations about existence and consciousness with my friends.

My linkedin is here… and a website I never update:

Ben Lawson

I am a first year Psychology PhD student interested in pursuing topics such as neuropolitics, automatically delivered mental health interventions, and opinion formation/maintenance. I have a diverse research background, having worked in allergy research at the Montreal Children’s Hospital, negative urgency/emotional regulation research pertaining to disordered eating at the Biopsychosocial Examination of Eating Patterns Lab, and on projects pertaining to implicit racial bias through independent collaboration with faculty at McGill University. Outside of my research interests I enjoy an array of history podcasts, going to the gym, and playing Dungeons & Dragons.

You can find me online at: LinkedIn - Twitter

Nathan Laundry

I combine the power of large language models, the IAI lab’s MOOClet framework for Adaptive Experimentation, and open education resource platforms to create a better learning and teaching experience for diverse populations. I envision open education resources that are easy to use, personalize explanations and interventions to students, and make it easy for instructors to identify the best teaching techniques for students.

Catch me at or on Medium

Lab Managers

Mahima Tirunelveli Santhakumar

I am one of the Lab Managers at the Intelligent Adaptive Interventions lab. I joined the lab after graduating with a Bachelor’s of Science in psychology from the University of Toronto. My current research interests involve studying cultural adaptations of interventions for people, especially youth, facing adversity and crisis. I am going to be pursuing my masters in counseling psychology. Outside of my research interests and work, I enjoy traveling the world, reading, and crocheting animals that look cursed but cute!

You can find me online at: LinkedIn - Twitter 

Yufei Wang (Kimis)

I am one of the Lab Managers at the Intelligent Adaptive Interventions lab. I joined the lab after graduating with a Bachelor’s of Science in computer science and cognitive science from the University of Toronto, during which I took an adventure to found/co-found EdTech startups and raised funds successfully. I will continue pursuing my interests in Mental Health, Education & Tech. Outside of my research interests and work, I love hiking in the mountains and reading with my 3 cats around. 

You can find me online at Linkedin or twitter  

Shakiba Rahnama

I have recently joined this amazing team as an Admin/Lab Manager. I graduated with an MEng in Construction Management in 2022, and since then, I've been exploring the world of startups and tech entrepreneurship. My biggest passion is using technology to help solve problems in mental health and education, and my ultimate goal is to create a company that focuses on these issues. I'm also an ethical vegan passionate about animal liberation and I love running, hiking in the mountains, and traveling.

You can reach me on LinkedIn and Twitter

Regular External collaborators

Audrey is an Assistant Professor in the Computer Science and Software Engineering department, and the Electrical Engineering and Computer Engineering department, at Université Laval. She is also affiliated with Mila — Quebec Artificial Intelligence Institute through a Canada CIFAR AI Chair. Her research aims at bridging the gap between theory and practice in reinforcement learning (RL).

I am a Researcher at the MRC Biostatistics Unit - University of Cambridge (UK), and a PhD Candidate in Methodological Statistics at Sapienza University of Rome (Italy). My research interests lie at the intersection between Bayesian methods, statistical reinforcement learning & multi-armed bandits, and modern real-world applications based on adaptive experimentation.

Dr. Sealfon brings considerable experience with physics education research, faculty development, and Bayesian cosmostatistics to her growing interest in learning analytics. She currently facilitates learning in the largest introductory physics courses at U of T (Physics 131 and 132). She aims to apply learning analytics to empower diverse learners through fostering both scientific reasoning and compassion.

I am a programme leader track at the MRC Biostatistics Unit (University of Cambridge, UK). My research involves the developing of design and analysis methodology for adaptive clinical trials that incorporates bandit dynamic optimisation ideas. I am interested particularly in the use of this novel approach to the design of clinical trials for rare diseases but also for the development of complex interventions such as digital therapeutics.

Anna Rafferty is an assistant professor at Carleton College in Computer Science. She completed her Ph.D. at the University of California, Berkeley, and was advised by Professor Tom Griffiths. Her recent work has been most concerned with how to apply machine learning and artificial intelligence techniques to improve education. Her projects have included developing algorithms to automatically diagnose students' understanding from their actions as well as more applied projects related to improving chemistry learning in the classroom.

Thomas W. Price is an Assistant Professor in the Computer Science Department at North Carolina State University, where he runs the HINTS Lab. His research goal is to re-imagine educational programming environments as adaptive, data-driven systems that support students automatically as they pursue learning goals that are meaningful to them. His lab develops intelligent, adaptive technology that use data to automatically support students learning to program. His research sits at the intersection of Computing Education Research (CER), Educational Data Mining (EDM) and Intelligent Tutoring Systems (ITS).

Rachel Kornfield

Hi, I’m Rachel! I am a Research Assistant Professor in Preventive Medicine at Northwestern University Feinberg School of Medicine and core faculty within the Center for Behavioral Intervention Technologies (CBITS). I completed my PhD in Communication at the University of Wisconsin-Madison (2018), followed by an NIMH-funded postdoctoral fellowship at CBITs (2018-2020). My current research program applies my multi-disciplinary training to design and test digital tools that can help individuals manage common mental health concerns like depression, anxiety, and substance use.

Jonah Meyerhoff

Dr. Meyerhoff’s program of research focuses on increasing access to suicide prevention care through the development and evaluation of technology-based preventive interventions. Dr. Meyerhoff employs human-centered design principles to partner with the lived experience community and design interventions that support suicide-related coping. He has a background in psychological intervention research for affective disorders with a specific emphasis on cognitive behavioral interventions for recurrent depressive disorders.

I am an Associate Professor jointly appointed by the Centre for Quantitative Medicine and the Program in Health Services and Systems Research, Duke-NUS Medical School, and the Department of Statistics and Applied Probability at the National University of Singapore. I also hold an Adjunct Associate Professor position with the Department of Biostatistics and Bioinformatics at Duke University.

Lisa Zhang

Lisa is an Assistant Professor, Teaching Stream in the Department of Mathematical and Computational Sciences at the University of Toronto Mississauga (UTM). She is interested in the long-term success of computer science students, in particular in improving student writing skills in computer science, and in improving student success in (and sometimes with) machine learning.

Bogdan Simion

Bogdan is an Assistant Professor, Teaching Stream in the Department of Mathematical and Computational Sciences at the University of Toronto Mississauga (UTM), with cross-appointment in the Institute for the Study of University Pedagogy. His teaching aims to spark student interest into computer science, from introductory programming courses, to exploring more advanced topics in the area of systems and databases. He pedagogic research spans a variety of topics, from analyzing the effects of active learning classrooms on student outcomes and perception, studying group work dynamics in the context of active learning, and analyzing the use of various help supports in introductory computer science. His recent interests also include curriculum mapping and assessment, writing skill development, and various behavioral design methods for fostering student learning and engagement.