Charles Cui

I’m a Ph.D. candidate in Computer Science at Northwestern University, advised by Matthew Kay. My research explores the intersection of algorithms, data science, and human-computer interaction. Currently, I’m interested in developing rigorous, adaptive methods for measurement and estimation problems in data visualization literacy and public health. I’m passionate about understanding and guiding human behavior and system design to bring positive social impact.

I co-organize the Mechanism Design for Social Good (MD4SG) research initiative, which leverages computational methods to improve equity and social welfare for marginalized groups. Additionally, I was a graduate fellow at Stanford’s RegLab where I developed computational methods for racial disparity estimation in public health. I also served as a Data Science for Social Good (DSSG) fellow at Carnegie Mellon University in 2022, where I worked with Vibrant Emotional Health to improve the service of the 988 Suicide & Crisis Lifeline through data-driven call routing.

C.V.

news

Mar 1, 2024	I will be joining the Population and Social Data Science Summer Incubator at the Max Planck Institute for Demographic Research in Germany from early June to late August this summer.
Feb 28, 2024	I was awarded the Intersection Science Fellowship by the Northwestern Institute on Complex Systems (NICO).
Feb 1, 2024	I will be in Hawaii in May for CHI We will be hosting a workshop on data visualization literacy. Please submit to our workshop or stop by to chat about data viz!
Feb 1, 2024	Our paper Odds and Insights: Decision Quality in Visual Analytics Under Uncertainty got accepted to ACM CHI 2024!

selected publications

2023

IEEE VIS

Adaptive Assessment of Visualization Literacy

Yuan Cui, Lily W. Ge, Yiren Ding, Fumeng Yang, Lane Harrison, and Matthew Kay

IEEE VIS, 2023

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Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and repeatable assessments of visualization literacy for individuals. However, current assessments, such as the Visualization Literacy Assessment Test (VLAT), are time-consuming due to their fixed, lengthy format. To address this limitation, we develop two streamlined computerized adaptive tests (CATs) for visualization literacy, A-VLAT and A-CALVI, which measure the same set of skills as their original versions in half the number of questions. Specifically, we (1) employ item response theory (IRT) and non-psychometric constraints to construct adaptive versions of the assessments, (2) finalize the configurations of adaptation through simulation, (3) refine the composition of test items of A-CALVI via a qualitative study, and (4) demonstrate the test-retest reliability (ICC: 0.98 and 0.98) and convergent validity (correlation: 0.81 and 0.66) of both CATs via four online studies. We discuss practical recommendations for using our CATs and opportunities for further customization to leverage the full potential of adaptive assessments.
ACM CHI

CALVI: Critical Thinking Assessment for Literacy in Visualizations

Lily W. Ge, Yuan Cui, and Matthew Kay

ACM CHI, 2023

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Visualization misinformation is a prevalent problem, and combating it requires understanding people’s ability to read, interpret, and reason about erroneous or potentially misleading visualizations, which lacks a reliable measurement: existing visualization literacy tests focus on well-formed visualizations. We systematically develop an assessment for this ability by: (1) developing a precise definition of misleaders (decisions made in the construction of visualizations that can lead to conclusions not supported by the data), (2) constructing initial test items using a design space of misleaders and chart types, (3) trying out the provisional test on 497 participants, and (4) analyzing the test tryout results and refining the items using Item Response Theory, qualitative analysis, a wrong-due-to-misleader score, and the content validity index. Our final bank of 45 items shows high reliability, and we provide item bank usage recommendations for future tests and different use cases.