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Matthew Kay

I am an Associate Professor in CS and Communication at Northwestern.

I work in human–computer interaction and information visualization.

My research includes work on visualizing uncertainty, usable statistics, and visualization literacy. I tackle problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques. I co-direct the Midwest Uncertainty Collective (MU collective), and I am the author of the tidybayes and ggdist R packages for uncertainty visualization. Previously, I was an Assistant Professor at UMSI.

For an up-to-date picture of my work, see the MU collective website. Some areas of my work (not necessarily current) include:

Communicating uncertainty: We are increasingly exposed to sensing and prediction in our daily lives (“how many steps did I take today?”, “how long until my bus shows up?”, “how much do I weigh?”). Uncertainty is both inherent to these systems and usually poorly communicated. To build understandable data presentations, we must study how people interpret their data and what goals they have for it. This informs the way that we should communicate results from our models, which in turn determines what models we must use in the first place. More…

Usable statistics: Science is failing all around us! Nothing replicates! Things may not be as dire as all that, but in fields like HCI and psychology, the statistical tools we use are failing us: these tools let users wander around without guidance and produce results without assisting users in interpretation. What would usable statistical tools look like? More…

Selected publications

See my C.V. or Google Scholar for a complete listing.

  1. In dice we trust: Uncertainty displays for maintaining trust in election forecasts over time

    Fumeng Yang, Chloe Rose Mortenson, Erik Nisbet, Nicholas Diakopoulos, and Matthew Kay

    • CHI 2024
    • Best paper award (top 1%)
    • PDF

  2. Watching the election sausage get made: How data journalists visualize the vote counting process in U.S. elections

    Mandi Cai and Matthew Kay

    • CHI 2024
    • Honorable mention (top 5%)
    • PDF

  3. Odds and insights: Decision quality in exploratory data analysis under uncertainty

    Abhraneel Sarma, Xiaoying Pu, Yuan Cui, Eli T Brown, Michael Correll, and Matthew Kay

    • CHI 2024
    • Honorable mention (top 5%)
    • PDF

  4. Swaying the public? Impacts of election forecast visualizations on emotion, trust, and intention in the 2022 U.S. midterms

    Fumeng Yang, Mandi Cai, Chloe Rose Mortenson, Hoda Fakhari, Ayse Deniz Lokmanoglu, Jessica Hullman, Steven L Franconeri, Nicholas Diakopoulos, Erik Nisbet, and Matthew Kay

    • VIS 2023
    • Best paper award (top 1%)
    • PDF

  5. ggdist: Visualizations of distributions and uncertainty in the grammar of graphics

    Matthew Kay

  6. Subjective probability correction for uncertainty representations

    Maryam Hedayati, Fumeng Yang, and Matthew Kay

    • CHI 2023
    • Honorable mention (top 5%)
    • PDF

  7. multiverse: Multiplexing alternative data analyses in R notebooks

    Abhraneel Sarma, Alex Kale, Michael Jongho Moon, Nathan Taback, Fanny Chevalier, Jessica Hullman, and Matthew Kay

  8. A probabilistic grammar of graphics

    Xiaoying Pu and Matthew Kay

  9. Visual reasoning strategies for effect size judgments and decisions

    Alex Kale, Matthew Kay, and Jessica Hullman

  10. Increasing the transparency of research papers with Explorable Multiverse Analyses

    Pierre Dragicevic, Yvonne Jansen, Abhraneel Sarma, Matthew Kay, and Fanny Chevalier

  11. The garden of forking paths in visualization: A design space for reliable exploratory visual analytics

    Xiaoying Pu, Matthew Kay

  12. Uncertainty displays using quantile dotplots or CDFs improve transit decision-making

    Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay

  13. Imagining replications: Graphical prediction & discrete visualizations improve recall & estimation of effect uncertainty

    Jessica Hullman, Matthew Kay, Yea-Seul Kim, and Samana Shrestha

  14. Researcher-centered design of statistics: Why Bayesian statistics better fit the culture and incentives of HCI

    Matthew Kay, Gregory Nelson, and Eric Hekler

  15. When (ish) is my bus? User-centered visualizations of uncertainty in everyday, mobile predictive systems

    Matthew Kay, Tara Kola, Jessica Hullman, and Sean Munson

  16. Beyond Weber’s Law: A second look at ranking visualizations of correlation

    Matthew Kay and Jeffrey Heer

  17. Unequal representation and gender stereotypes in image search results for occupations

    Matthew Kay, Cynthia Matuszek, and Sean Munson

  18. How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy

    Matthew Kay, Shwetak N. Patel, and Julie A. Kientz

  19. Challenges in personal health tracking: The data isn’t enough

    Matthew Kay

  20. There’s no such thing as gaining a pound: Reconsidering the bathroom scale user interface

    Matthew Kay, Dan Morris, mc schraefel, and Julie A. Kientz

    • Ubicomp 2013
    • Best paper award (top 1%)
    • PDF
    • BibTeX

  21. PVT-Touch: Adapting a reaction time test for touchscreen devices

    Matthew Kay, Kyle Rector, Sunny Consolvo, Ben Greenstein, Jacob O. Wobbrock, Nathaniel F. Watson, and Julie A. Kientz

  22. Lullaby: A capture & access system for understanding the sleep environment

    Matthew Kay, Eun Kyoung Choe, Jesse Shepherd, Benjamin Greenstein, Nathaniel Watson, Sunny Consolvo, and Julie A. Kientz

    • Ubicomp 2012
    • Best paper award (top 1%)
    • PDF
    • BibTeX

  23. Textured agreements: Re-envisioning electronic consent

    Matthew Kay and Michael Terry

  24. Communicating software agreement content using narrative pictograms

    Matthew Kay and Michael Terry

  25. Perceptions and practices of usability in the Free/Open Source Software (FOSS) community

    Michael Terry, Matthew Kay, and Ben Lafreniere

  26. Ingimp: Introducing instrumentation to an end-user open source application

    Michael Terry, Matthew Kay, Brad Van Vugt, Brandon Slack, and Terry Park