Senior Principal Research Manager
Hybrid meetings limit inclusion for remote participants. The Hybridge experimental system provides different interfaces for remote and room endpoints, focusing on improving inclusion via shared spatiality and remote…
Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others…
Effective meetings are effortful, but traditional videoconferencing systems offer little support for reducing this effort across the meeting lifecycle. Generative AI (GenAI) has the potential to radically redefine…
A key component of team performance is participation among group members. One widespread organizational function that provides a stage for participation is the workplace meeting. With the shift…
Research
I am a social scientist of human-computer interaction (opens in new tab), exploring remote and hybrid team collaboration and AI workflows at Microsoft Research. I work in Brisbane, Australia, reporting to the Cambridge UK Lab.
I currently co-lead the Tools for Thought (opens in new tab) team, which aims to put human cognition at the heart of our AI systems. Within that team, I lead the Intentional Meetings (opens in new tab) workstream, which investigates how to evolve purposful meeting systems, behaviors, and cultures with Generative AI.
Approach
My training is ethnomethodological, drawing on video-recorded conversations and ethnographic data, and analysing that data using qualitative methods such as conversation analysis and membership categorisation analysis.
I specialize in field research but also conduct interview, diary, and survey studies, as well as lab-based studies of prototypes. Increasingly, however, I combine qualitative with quantitative approaches, such as surveys and telemetry, to answer questions at scale.
Hackathons
I have been a member of 5 global category first-place winning projects in Microsoft OneWeek Hackathons, including one global Grand Prize winner.
You can read about one of these that became a feature in our Garage Wall of Fame post Mobile Sharing and Companion Experiences for Microsoft Teams Meetings (opens in new tab). One day I might even be able to say what the other ones were! 😉
Academic
I received my PhD in 2010 in the field of Sociology specializing in Communication, from the University at Albany, State University of New York (opens in new tab). My dissertation was chaired by Professor Emerita Anita Pomerantz with committee members Professor Teresa Harrison, Professor Glenna Spitze, and Professor Ronald Jacobs.
Prior to working at Microsoft, I was a Lecturer in Strategic Communication at The University of Queensland (opens in new tab), Brisbane, Australia.
I have been a PC member of CHI and CSCW several times and review for many major HCI, communication, and technology journals and conferences. I was the Senior Editor of the Communication Technology section of the online Oxford Research Encyclopedia of Communication. I have guest-edited special issues of Human Computer Interaction, the Electronic Journal of Communication. and the Australian Journal of Communication. I co-chaired the Microsoft 2020 New Future of Work Symposium (opens in new tab) (with Gloria Mark) and the 2012 conference of the Australasian Institute of Ethnomethodology and Conversation Analysis (with Richard Fitzgerald).
Digital Advocacy
I am the former Chair and a former Board Member of Electronic Frontiers Australia, a non-profit group advocating for digital access, freedom, and privacy.
Generative AI tools can help users with many tasks. One such task is data analysis, which is notoriously challenging for non-expert end-users due to its expertise requirements, and where AI holds much potential, such as finding relevant data sources, proposing analysis strategies, and writing analysis code. To understand how data analysis workflows can be assisted or impaired by generative AI, we conducted a study (n=15) using Bing Chat via participatory prompting. Participatory prompting is a recently developed methodology in which users and researchers reflect together on tasks through co-engagement with generative AI. In this paper we demonstrate the value of the participatory prompting method. We found that generative AI benefits the information foraging and sensemaking loops of data analysis in specific ways, but also introduces its own barriers and challenges, arising from the difficulties of query formulation, specifying context, and verifying results.
Meetings should be meaningful and effective. Technology can help.
Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. We argue that metacognition – the psychological ability to monitor and control one’s thoughts and behavior – offers a valuable lens to understand and design for these usability challenges.
Ineffective meetings due to unclear goals are major obstacles to productivity, yet support for intentionality is surprisingly scant in our meeting and allied workflow technologies. To design for intentionality, we need to understand workers’ attitudes and practices around goals. We interviewed 21 employees of a global technology company and identified contrasting mental models of meeting goals: meetings as a means to an end, and meetings as an end in themselves. We explore how these mental models impact how meeting goals arise, goal prioritization, obstacles to considering goals, and how lack of alignment around goals may create tension between organizers and attendees. We highlight the challenges in balancing preparation, constraining scope, and clear outcomes, with the need for intentional adaptability and discovery in meetings. Our findings have implications for designing systems which increase effectiveness in meetings by catalyzing intentionality and reducing tension in the organisation of meetings.
Work video meetings in the traditional grid interface have inclusion, effectiveness, and fatigue problems, due in part to the difficulty of directing or communicating attention. Virtual 3D meeting spaces have value, but representing people in them is a challenge. Avatars face resistance, and 2D video is limited to near-frontal views, constraining the spatial layout. We present a novel experimental system for virtual meeting rooms that predicts 3D video of users in real-time from a standard webcam, positions them in a shared 3D space, and renders a controllable first-person view. We report study results comparing this system to a traditional grid, and to 2D video of people in the same 3D space. While spatial layouts fared better in terms of attention and co-presence, the traditional grid was more comfortable and professional. This is likely due to unsettled 3D design, the need for manual control, and a preference for the familiar.
