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The COoKIE Group

Communication, Organization of Knowledge, Information Ecosystems 

What we Do

The COoKIE is an interdisciplinary Human-Computer Interaction research group focusing on Human-AI communication and collaboration.

Our recent work touches on diverse technologies, including conversational agents, large language models, generative AI systems, and AI-based decision support systems. Seeking to advance Human-AI interaction design, we explore mechanisms driving the perceptions of different types of AI systems, such as trust, anthropomorphization, or perceived reliability to inform the system and algorithm design requirements for effective, efficient, and ethical AI.

Our Work is Supported by:

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The Natural Sciences and Engineering Research Council of Canada (NSERC)

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The Social Sciences and Humanities Research Council of Canada (SSHRC)

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The Naver Corporation,

South Korea

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The Faculty of Information,

University of Toronto, Canada

Current Projects

Human-Agent Communication

The growing complexity of intelligent systems creates a promising environment for the shift in a user experience paradigm: from pure human-system interaction to human-system communication and collaboration. This is further amplified by the increased popularity of entity-based system interfaces (artificial agents) and the integration of conversational user interfaces (CUI). The human-agent communication paradigm opens exciting opportunities to exploit a computer's unique abilities to complement humans. However, first, we need to understand the specifics of human-agent communication.

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AI-Based Decision Support Systems (ADS)

AI-integrated decision-making support systems allow to augment and extend human capabilities, improving the quality and efficiency of decisions. These systems use AI to learn, remember, analyze, and reason based on large amounts of often complex data. The successful use of these features requires an understanding of how they can appropriately complement human processes. To address these questions, we explore what aspects of augmentation are required in different scenarios, how the outcomes should be communicated and explained (xAI), and how to properly calibrate and, if needed, repair trust in systems' performance. 

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Information Architecture for Conversational UI

The intuitive nature of Conversational User Interfaces (CUI), fashioned after natural human conversation, allows for convenient and fast input and can serve as a single-channel input for multiple services, devices, applications, and even environments. However, due to the absence of visual clues, affordances, and spatial organization, the use of CUI is associated with a decreased sense of control over the information dynamics. We explore the perceived “locations”, “structure“, and “paths” of the information shared through CUI, to inform the required information architecture. 

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Meet the Team

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Anastasia Kuzminykh

Assistant Professor,

Faculty of Information, UofT

Director of The COoKIE Group and the Toronto Human-AI Interaction Research School (THAI RS)

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Nazar Ponochevnyi

Undergrad. Research Intern,

Faculty of Information, UofT

Developing Voice Interface for Chart Creation

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Paula Aoyagui

Research Assistant, Previously Master's Student, Faculty of Information, UofT

Explainable AI Strategies in Non-Ground-Truth Cases

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Batool Fatima

Master's Reading Student,

Faculty of Information, UofT

Trust Mechanisms in Human-AI Collaboration

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Christina Wei

Ph.D. Student, THAI RS'22,

Faculty of Information, UofT

Conversation Architecture for Artificial Agents

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Yuxin Xu

Master's Thesis Student,

Faculty of Information, UofT

Image Accessibility for Visually Impaired Users

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Rimsha Rizvi

Master's Research Assistant, Reading Student, Faculty of Information, UofT

Fairness Mitigation in Human-AI Collaboration

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Omer Imran

Master's Reading Student,

Faculty of Information, UofT

Trust Mechanisms in Human-AI Collaboration

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Rezvan Boostani

Ph.D. Student,

Faculty of Information, UofT

Artificial Agents for Financial Assistants for Older Adults

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Fatima Zohra

Master's Thesis Student,

Faculty of Information, UofT

Accuracy Preserving Fairness Mitigation Techniques for Financial ML Models

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Kshitij Anand

Master's Reading Student, Faculty of Information, UofT

Conversational Information Architecture in Human-AI Collaborative Tasks

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Janna Cameron

Master's Reading Student,

Faculty of Information, UofT

Trust Calibration Design Techniques in Human-AI Collaboration

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Senjuti Dutta

Ph.D. Student, Electrical Eng. & Computer Science, UTK

Intelligent Systems to Support Crowdwork Flexibility

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Janet Lu

Master's Thesis Student,

Faculty of Information, UofT

Conversational Agents for Co-Design Assistance 

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Shradha Anand

Master's Research Assistant,

Faculty of Information, UofT

Document Exploration Through Conversational Interfaces

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Juan Antonio Nelson

Master's Research Assistant,

Faculty of Information, UofT

Automatic Speech Detection with a focus on Caribbean Dialects

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Manveer Kalirai

Ph.D. Student, THAI RS'22, Faculty of Information, UofT

Information Architecture for Smart Homes

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Nabila Chowdhury

PhD Research Assistant, THAI RS'22, Faculty of Information, UofT

Promt-Based Bias in AI Generated Art

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Mohammed Elkhechen

Master's Research Assistant, 

Faculty of Information, UofT

Psychological Ownership in Human-AI Collaboration

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Nimisha Karnatak

Ph.D. Student, Faculty of Information, UofT

Explainable Medical AI for Doctors in Rural Areas

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Sharon Ferguson

PhD Research Assistant, THAI RS'21, Mechanical and Industrial Engineering, UofT

Architecture of AI Explanations of Subtle Sexism

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Tanisha Amarakoon

Master's Research Assistant,

Faculty of Information, UofT

Psychological Ownership in Human-AI Collaboration

Team

Alumni

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Hidaya Ismail

Research Assistant,

Faculty of Information, UofT

Psychological Ownership in Human-AI Collaboration

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Mona Abou Swaid

Master's Research Assistant,

Faculty of Information, UofT

Sense of Ownership Over Digital Artifacts

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Deepak Surya

Mitacs Globalink Research Intern, 2022, Faculty of Information, UofT

Text-to-Voice Conversation Style Transformation

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Akriti Kaur

Master's Research Assistant,

Faculty of Information, UofT

Document Exploration Through Conversational Interfaces

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Kusum Grandhi

Mitacs Globalink Research Intern, 2022, Faculty of Information, UofT

Modality-Dependant Decision Effects of AI Explanations

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Lizhen Ying

Master's Reading Student,

Faculty of Information, UofT

Anthropomorphization of Disembodied Agents

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Kelly McConvey

Ph.D. Research Assistant,

Faculty of Information, UofT

Collaboration with AI Models in Education

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Mujgan Ozceylan

Master's Research Assistant, Reading Student, THAI RS'21

Faculty of Information, UofT

AI Explanations of Subtle Sexism

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Lakshya

Master's Reading Student,

Faculty of Information, UofT

Designing Human-AI Conversation

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Julia Gil

Undergrad Reading Student,

Faculty of Information, UofT

User Communication with Conversational Agents

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Jinchi Lin

Master's Reading Student,

Faculty of Information, UofT

Personalization of Disembodied Intelligent Agents

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Ruijia Yang

Undergrad Reading Student,

Computer Science, UofT

Simulation As a

Research Method in HCI

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Bill Than

NSERC USRA, BI student,

Faculty of Information, UofT

Engagement Dynamics for Teleconferencing Classrooms During Covid-19

Join the Team

PostDocs 

We would be happy to consider applications from potential postdoc candidates. Typically, a postdoc contract is offered for a year with a potential consequent extension for another year. Currently, we are looking for postdoc candidates, broadly interested in Human-AI Collaboration with a particular focus on conversational interactions. If you are interested in a postdoc in our group, please send your CV, writing samples, and a brief statement of research interest to Prof. Anastasia Kuzminykh (anastasia.kuzminykh@utoronto.ca).

Ph.D. Program 

We invite applications from candidates with a Master's degree in Computer Science, Systems Design Engineering, Data Science, Psychology, Communication Studies, or similar fields. Research experience is expected from the applicants, academic publications are a plus. The application period is typically open September-November, with the entry into the program once a year, in September. Please review the admission requirements to the Ph.D. in Information Program. Prior to submitting your application, it is highly recommended to contact your potential supervisor and have a discussion about your research plans and interests. If you are interested in a Ph.D. in our group, please send your CV, writing samples, and a brief statement of research interest to Prof. Anastasia Kuzminykh (anastasia.kuzminykh@utoronto.ca).

Master's Thesis

The Master's Thesis option at the COoKIE group is available to students, enrolled in the Master's of Information program at the iSchool, University of Toronto. Please review the pre-requisites and eligibility for the master's thesis. To inquire about the reading course and/or the thesis work at the COoKIE Group, please send your CV and a brief statement of research interest to Prof. Anastasia Kuzminykh (anastasia.kuzminykh@utoronto.ca). If applicable, please mention any relevant experience, including prior research experience and/or reading courses taken in the past.

RAship

In most of the academic terms (September-December, January-April, May-August), the COoKIE Group has 1-2 Research Assistantship positions available for Master's or undergraduate level students. While typically we hire students from the University of Toronto, external applications can occasionally be considered. The standard RA contract in our group includes 12 weeks, 5-6 hours per week, working on an individual or a group project. To inquire about open positions and to apply for RAship, please contact Prof. Anastasia Kuzminykh (anastasia.kuzminykh@utoronto.ca) no later than a week before the beginning of an academic term. Please include your CV and a brief statement of research interest.

Our Collaborators

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