Midwest Uncertainty Collective

Midwest Uncertainty Collective

 

Midwest Uncertainty Collective

 

We are a research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. Topics we like include sampling-oriented uncertainty visualizations, eliciting and modeling beliefs about data, automated visualization reasoning and generation, and Bayesian statistics.

The MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. Topics we like include sampling-oriented uncertainty visualizations, eliciting and modeling beliefs about data, automated visualization reasoning and generation, and Bayesian statistics.

The MU Collective is directed by Jessica Hullman) and Matt Kay of Northwestern University.

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. Topics we like include sampling-oriented uncertainty visualizations, eliciting and modeling beliefs about data, automated visualization reasoning and generation, and Bayesian statistics.

The MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University.

We are a research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. Topics we like include sampling-oriented uncertainty visualizations, eliciting and modeling beliefs about data, automated visualization reasoning and generation, and Bayesian statistics.

The MU Collective is directed by Jessica Hullman and Matthew Kay of Northwestern University.

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. Topics we like include sampling-oriented uncertainty visualizations, eliciting and modeling beliefs about data, automated visualization reasoning and generation, and Bayesian statistics.

The MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University.

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to improve both experts' and lay people's abilities to reason about data through visual representations that align with how people think. Topics we like include sampling-oriented uncertainty visualizations, interactive visualization for thinking about priors, multiple views, and Bayesian statistics.

MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University.

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to improve both experts' and lay people's abilities to reason about data through visual representations that align with how people think. Topics we like include sampling-oriented uncertainty visualizations, interactive visualization for thinking about priors, multiple views, and Bayesian statistics.

MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University.

We are a cross-institution research lab working at the intersection of information visualization and uncertainty communication. Our mission is to improve both experts' and lay people's abilities to reason about data through visual representations that align with how people think. Topics we like include sampling-oriented uncertainty visualizations, interactive visualization for thinking about priors, multiple views, and Bayesian statistics.

MU Collective is directed by Jessica Hullman and Matt Kay of Northwestern University.

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Recent Papers | See all

Recent Papers | See all

Designing Shared Information Displays for Agents of Varying Strategic Sophistication
Dongping Zhang, Jason Hartline, Jessica Hullman
ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) 2024 | PDF

Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms
Fumeng Yang, Mandi Cai, Chloe Mortenson, Hoda Fakhari, Ayse D. Lokmanoglu, Jessica Hullman, Steven Franconeri, Nicholas Diakopoulos, Erik C. Nisbet, Matthew Kay
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | BEST PAPER AWARD | PDF

Adaptive Assessment of Visualization Literacy
Yuan Cui, Lily W. Ge, Yiren Ding, Fumeng Yang, Lane Harrison, Matthew Kay
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics
Matthew Kay
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

Dupo: A Mixed-Initiative Authoring Tool for Responsive Visualization
Hyeok Kim, Ryan Rossi, Jessica Hullman, Jane Hoffswell
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

The Rational Agent Benchmark for Data Visualization
Yifan Wu, Ziyang Guo, Michails Mamakos, Jason Hartline, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

EVM: Incorporating Model Checking into Exploratory Visual Analysis
Alex Kale, Ziyang Guo, Xiao Li Qiao, Jeffrey Heer, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Hariharan Subramonyam, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2023 | PDF

Causal Quartets: Different Ways to Obtain the Same Average Treatment Effect
Andrew Gelman, Jessica Hullman, Lauren Kennedy
American Statistician | PDF

CALVI: Critical Thinking Assessment for Literacy in Visualizations
Lily W. Ge, Yuan Cui, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2023 |  BEST PAPER HONORABLE MENTION | PDF

multiverse: Multiplexing Alternative Data Analyses in R Notebooks
Abhraneel Sarma, Alex Kale, Michael Moon, Nathan Taback, Fanny Chevalier, Jessica Hullman, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2023 |  BEST PAPER HONORABLE MENTION | PDF

How Data Analysts Use a Visualization Grammar in Practice
Xiaoying Pu, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2023 | PDF

MetaExplorer: Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis
Alex Kale, Sarah Lee, Terrance Goan, Elizabeth Tipton, Jessica Hullman
ACM Human Factors in Computing Systems (CHI) 2023 | PDF | GITHUB

Subjective Probability Correction for Uncertainty Representations
Fumeng Yang, Maryam Hedayati, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2023 |  BEST PAPER HONORABLE MENTION | PDF

What’s Driving Conflicts Around Differential Privacy for the U.S. Census
Priyanka Nanayakkara, Jessica Hullman
IEEE Security & Privacy Magazine | PDF

The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance
Russell Davis, Xiaoying Pu, Yiren Ding, Brian D. Hall, Karen Bonilla, Mi Feng, Matthew Kay, and Lane Harrison
IEEE Transactions on Visualization and Computer Graphics | PDF

Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in Scatterplots
Abhraneel Sarma, Shunan Guo, Jane Hoffswell, Ryan Rossi, Fan Du, Eunyee Koh, and Matthew Kay
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2022 | PDF

The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society | PDF

Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews
David Liu, Priyanka Nanayakkara, Sarah Ariyan Sakha, Grace Abuhamad, Su Lin Blodgett, Nicholas Diakopoulos, Jessica R. Hullman, Tina Eliassi-Rad
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society | PDF

A Survey of Tasks and Visualizations in Multiverse Analysis Reports
Brian D. Hall, Yang Liu, Yvonne Jansen, Pierre Dragicevic, Fanny Chevalier, Matthew Kay
Computer Graphics Forum | PDF

Cicero: A Declarative Grammar for Responsive Visualization
Hyeok Kim, Ryan Rossi, Fan Du, Eunyee Koh, Shunan Guo, Jessica Hullman, Jane Hoffswell
ACM Human Factors in Computing Systems (CHI) 2022 | PDF

Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases
Priyanka Nanayakkara, Johes Bater, Xi He, Jessica Hullman, and Jennie Rogers
Proceedings on Privacy Enhancing Technologies 2022 | PDF

Designing for interactive exploratory data analysis requires theories of graphical inference
Jessica Hullman, Andrew Gelman
Harvard Data Science Review 2021 | PDF

Unpacking the Expressed Consequences of AI Research in Broader Impact Statements
Priyanka Nanayakkara, Jessica Hullman, Nicholas Diakopoulos
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 2021 | PDF

Visualization Equilibrium
Paula Kayongo, Glenn Sun, Jason Hartline, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2021 | PDF

Visualizing Uncertainty in Probabilistic Graphs with Network Hypothetical Outcome Plots (NetHOPs)
Dongping Zhang, Eytan Adar, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2021 | PDF

Causal Support: Modeling Causal Inferences with Visualizations
Alex Kale, Yifan Wu, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2021 | PDF

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization
Hyeok Kim, Ryan Rossi, Abhraneel Sarma, Dominik Moritz, and Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. VIS) 2021 | PDF

Design Patterns and Trade-Offs in Responsive Visualizationfor Communication
Hyeok Kim, Dominik Moritz, Jessica Hullman
EUROVIS 2021 | PDF

Information, incentives, and goals in election forecasts
Andrew Gelman, Jessica Hullman, Christopher Wlezien, George Elliott Morris
Judgment and Decision Making 2020 | PDF

Revealing Perceptual Proxies with Adversarial Examples
Brian D. Ondov, Fumeng Yang, Matthew Kay, Niklas Elmqvist, Steven Franconeri
IEEE Trans. Visualization & Comp. Graphics (Proc. INFOVIS) 2020 | PDF

Bayesian-Assisted Inference from Visualized Data
Yea-Seul Kim, Paula Kayongo, Madeleine Grunde-McLaughlin, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. INFOVIS) 2020 | PDF

Visual Reasoning Strategies for Effect Size Judgments and Decisions
Alex Kale, Matthew Kay, Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. INFOVIS) 2020 | BEST PAPER AWARD | PDF

Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
Sungsoo Ray Hong, Jessica Hullman, Enrico Bertini
ACM Computer Supported Coopoerative Work (CSCW) 2020 | PDF

How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results
Jake M. Hofman, Dan Goldstein, Jessica Hullman
ACM Human Factors in Computing Systems (CHI) 2020 |  BEST PAPER HONORABLE MENTION |  PDF

Prior Setting in Practice: Strategies and Rationales Used in Choosing Prior Distributions for Bayesian Analysis
Abhraneel Sarma, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2020 | PDF | GITHUB

A Probabilistic Grammar of Graphics
Xiaoying Pu, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2020 |  BEST PAPER HONORABLE MENTION |  PDF | GITHUB

Exploring the Effects of Aggregation Choices on Untrained Visualization Users’ Generalizations from Data
Francis Nguyen, Xiaoli Qiao, Jeffrey Heer, Jessica Hullman
Computer Graphics Forum 2020 | PDF |   GITHUB

Why Authors Don't Visualize Uncertainty
Jessica Hullman
IEEE Trans. Visualization & Comp. Graphics (Proc. INFOVIS) 2019 |  PDF

Increasing the Transparency of Research Papers with Explorable Multiverse Analyses
Pierre Dragicevic, Yvonne Jansen, Abhraneel Sarma, Matthew Kay, and Fanny Chevalier
ACM Human Factors in Computing Systems (CHI) 2019 |  BEST PAPER AWARD  | PDF | WEBSITE | GITHUB

A Bayesian Cognition Approach to Improve Data Visualization
Yea-Seul Kim, Logan A Walls, Peter Krafft, Jessica Hullman
ACM Human Factors in Computing Systems (CHI) 2019 | PDF | GITHUB

Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths
Alex Kale, Matthew Kay, Jessica Hullman
ACM Human Factors in Computing Systems (CHI) 2019PDF | GITHUB

Vocal Shortcuts for Creative Experts
Yea-Seul Kim, Mira Dontcheva, Eytan Adar, Jessica Hullman
ACM Human Factors in Computing Systems (CHI) 2019 | PDF