About

Matthew Kay is an Associate Professor jointly appointed in the Departments of Computer Science and Communication Studies at Northwestern University. His research focuses on human-computer interaction and information visualization, with specific interests in uncertainty visualization, personal health informatics, and the design of human-centered tools for data analysis. His work is supported by multiple NSF awards and has earned several best paper awards across venues such as CHI, InfoVis, UbiComp, and MobileHCI. Kay co-directs the Midwest Uncertainty Collective and is the author of the R packages tidybayes and ggdist, which are used for visualizing Bayesian statistical model output and uncertainty.

Research topics

• Computer Science
• Artificial Intelligence
• Geography
• Environmental health
• Cartography
• Statistics
• Demography
• Medicine
• Virology
• Mathematics

Selected publications

• Through a Live Elections Dashboard, Darkly: Managing Expectations and Trust in Progressive Vote Counting During the 2024 U.S. Election

  Open Science Framework · 2026-01-01

  articleOpen accessSenior author

  During U.S. elections, news outlets publish live dashboards to contextualize vote counting and manage public expectations. This proved challenging in 2020 amid election fraud allegations, sparking conversations about how data journalists might better visualize and explain live vote counting. To address this, we designed a dashboard to foster understanding of the progressive nature of vote counts and more realistic expectations of the vote counting timeline. We deployed it during the 2024 U.S. presidential election, showing it to 308 people with real results, and collected surveys and interviews on impressions and trust. We contribute: (1) a design process and framework for how audiences might form expectations around live data, (2) survey findings suggesting live forecasts slightly increased confidence in vote counting and slightly reduced belief in evidence of fraud, and (3) interview findings underscoring the importance of agency in viewing live data and tensions in the perceived usefulness of live forecasts.

  PublisherDOI

• Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs

  Open MIND · 2026-02-05

  preprint

  Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large language models (MLLMs) that helps designers explore detailed census-based personas, extract product features inferred from persona attributes, and recombine them for specific customer segments. In a study with 12 professional designers, we show that Personagram facilitates more actionable ideation workflows by structuring multimodal thinking from persona attributes to product design features, achieving higher engagement with personas, perceived transparency, and satisfaction compared to a chat-based baseline. We discuss implications of integrating AI-generated personas into product design workflows.

  DOI

• Data Literacy for the 21st Century: Perspectives from Visualization, Cognitive Science, Artificial Intelligence, and Education

  2026-04-13

  articleSenior author

  Data literacy is becoming a foundational skillset in our increasingly data-driven society. Fields that rely heavily on data, such as data visualization, cognitive psychology, and artificial intelligence, each contribute unique perspectives in studying data literacy. Yet, these efforts remain siloed. Researchers studying visualization literacy and AI literacy have recently run separate workshops at ACM CHI, each independently calling for interdisciplinary conversations. Such conversations would not only advance the study of data literacy but also inform research agendas within each individual discipline. Building on ACM CHI’s track record of attracting diverse researchers across many disciplines, we propose a workshop that connects these literacies through the common ground of data literacy. We invite interdisciplinary perspectives by bringing researchers, practitioners, and students together with expertise from fields including HCI, data visualization, psychology, AI, and learning sciences to develop a holistic framework for understanding, measuring, and teaching data literacy that is grounded in real-world applications.

  PublisherDOI

• Through a Live Elections Dashboard, Darkly: Managing Expectations and Trust in Progressive Vote Counting During the 2024 U.S. Election

  2026-04-13 · 1 citations

  articleSenior author
  PublisherDOI

• Inoculating Against Visualization Misinformation Through Gamification

  2026-04-13

  articleOpen accessSenior author

  Data visualizations, if not appropriately designed, can be ill-formed and exacerbate the spread of misinformation. Viewers must be equipped with the skills to navigate around ill-formed visualizations. Yet, existing studies for improving visualization literacy are focused on the interpretation and creation of well-formed visualizations. We ran a series of workshops with high school students to test out design variants of an educational serious game to ultimately help them build “immunity” against misleading visualizations. We iteratively refined the game design based on feedback and observations from these workshops. We reflect on our individual, competitive, and team-based game design variants to outline the promising features for future iterations and challenges that warrant further exploration. Together, these workshops serve as case studies that allow us to take informed next steps toward developing an engaging serious game to combat visualization misinformation. Materials and outputs from the workshops are available at: https://osf.io/56ac4/.

  PublisherDOI

• Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs

  ArXiv.org · 2026-02-05

  articleOpen access

  Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large language models (MLLMs) that helps designers explore detailed census-based personas, extract product features inferred from persona attributes, and recombine them for specific customer segments. In a study with 12 professional designers, we show that Personagram facilitates more actionable ideation workflows by structuring multimodal thinking from persona attributes to product design features, achieving higher engagement with personas, perceived transparency, and satisfaction compared to a chat-based baseline. We discuss implications of integrating AI-generated personas into product design workflows.

  PublisherOA PDF

• Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization

  2025-02-11

  preprintOpen access

  Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style transfer. We explore the application of such metrics to judgments of visualization similarity. We extend a similarity metric using five ML architectures and three pre-trained weight sets. We replicate results from previous crowdsourced studies on scatterplot and visual channel similarity perception. Notably, our metric using pre-trained ImageNet weights outperformed gradient-descent tuned MS-SSIM, a multi-scale similarity metric based on luminance, contrast, and structure. Our work contributes to understanding how deep-feature-based metrics can enhance similarity assessments in visualization, potentially improving visual analysis tools and techniques. Supplementary materials are available at https://osf.io/dj2ms/.

  PublisherDOI

• Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization

  2025-04-25

  preprintOpen access

  Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems.Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style transfer.We explore the application of such metrics to judgments of visualization similarity.We extend a similarity metric using five ML architectures and three pre-trained weight sets.We replicate results from previous crowdsourced studies on scatterplot and visual channel similarity perception.Notably, our metric using pre-trained ImageNet weights outperformed gradient-descent tuned MS-SSIM, a multi-scale similarity metric based on luminance, contrast, and structure.Our work contributes to understanding how deep-feature-based metrics can enhance similarity assessments in visualization, potentially improving visual analysis tools and techniques.Supplementary materials are available at https://osf.io/dj2ms/.

  PublisherOA PDFDOI

• The State of the Art in Visualization Literacy

  ArXiv.org · 2025-08-31

  preprintOpen access

  Research in visualization literacy explores the skills required to engage with visualizations. This state-of-the-art report surveys the current literature in visualization literacy to provide a comprehensive overview of the field. We propose a taxonomy of visualization literacy that organizes the field into competency themes and research categories. To address ambiguity surrounding the term ``visualization literacy'', we provide a framework for operationalizing visualization literacy based on application contexts (including domain, scenario, and audience) and relevant competencies, which are categorized under consumption, construction, critique, and connection. Research contributions are organized into five categories: ontology, assessment, mechanisms, populiteracy, and intervention. For each category, we identify key trends, discuss which competencies are addressed, highlight open challenges, and examine how advancements within these areas inform and reinforce each other, driving progress in the field.

  PublisherOA PDFDOI

• An Autoethnography on Visualization Literacy: A Wicked Measurement Problem

  2025-08-11

  preprintOpen accessSenior author

  We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a "wicked" problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity.

  PublisherDOI

Recent grants

• CHS: Small: Collaborative Research: Validating and Communiciating Model-Based Approaches for Data Visualization Ability Assessment

  NSF · $239k · 2018–2021

• CHS: Small: Developing a Probabilistic Grammar of Graphics for Flexible Uncertainty Visualization

  NSF · $500k · 2019–2021

• CHS: Small: Collaborative Research: Validating and Communiciating Model-Based Approaches for Data Visualization Ability Assessment

  NSF · $122k · 2020–2023

Frequent coauthors

• Matthew J. Gadlage

  Naval Surface Warfare Center

  31 shared

• Abhraneel Sarma

  Northwestern University

  31 shared

• Adam R. Duncan

  Naval Sea Systems Command

  30 shared

• Jessica Hullman

  Northwestern University

  28 shared

• Michael Correll

  Northeastern University

  22 shared

• Xiaoying Pu

  Northeastern University

  22 shared

• Véronique Ferlet-Cavrois

  European Space Research and Technology Centre

  18 shared

• Fumeng Yang

  16 shared

Education

• PhD, Computer Science and Engineering

  University of Washington

  2016

Awards & honors

• Multiple NSF awards
• Best paper awards across human-computer interaction and info…