UW-Madison
Collaborative Computing Group

We are a research group at the Information School at the University of Wisconsin.

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Research Areas

We conduct research at the intersection of human-computer interaction, social computing, and information science.

Social Computing

Social interactions are at the center of human activity. Understanding how collectives of teams, communities, organizations, and markets disseminate information using technology is crucial to our understanding of computing.

Bias

Systemic disparities (racial disparities, gender disparities, economic disparities, urban/rural disparities) permeate social systems online. This research trust seeks to combat these systemic disparities by focusing on two primary problems: (1) causally understanding when/why systemic disparities do or do not occur, and (2) building sociotechnical systems to intervene on the production of these disparities.

Citizen Science

Public participation in scientific research (PPSR) has dramatically transformed the scientific enterprise. To enhance citizen science requires that we understand the capabilities of both amateurs and the technologies that mediated collaborations.

Health Informatics

Information about health is personal, sensitive, and complex. We study how people generate, manage, and make sense of diverse types of health information, both individually and in groups.

Recent Publications

Understanding Wikipedia practices through Hindi, Urdu, and English takes on an evolving regional conflict Molly Hickman, Viral Pasad, Harsh Kamalesh Sanghavi, Jacob Thebault-Spieker, Sang Won Lee, Proceedings of the ACM Human Computer Interaction 2021
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Shifting Forms of Engagement: Volunteer Learning in Online Citizen Science Corey Jackson, Carsten Ă˜sterlund, Kevin Crowston, Mahboobeh Harandi, Laura Trouille, Proceedings of the ACM on Human-Computer Interaction 2020
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Electronic health records in ophthalmology: source and method of documentation Brad Henriksen, Isaac Goldstein, Adam Rule, Abigail Huang, Haley Dusek, Austin Igelman, Michael Chiang, Michelle Hribar, American Journal of Ophthalmology 2020
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Redundancy of Progress Notes for Serial Office Visits Michelle Hribar, Adam Rule, Abigail Huang, Haley Dusek, Isaac Goldstein, Brad Henriksen, Wei-Chun Lin, Austin Igelman, Michael Chiang, Ophthalmology 2020
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The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. Mahboobeh Harandi, Kevin Crowston, Corey Jackson, Carsten Ă˜sterlund, Hawaii International Conference on System Sciences 2020
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