Strong Northwestern CS Presence at the 2023 IEEE VIS Conference
Faculty, students, postdocs, and alumni participated in the annual forum for advances in theory, methods, and applications of visualization and visual analytics
Northwestern Computer Science and the Midwest Uncertainty Collective (MU Collective) had a strong presence at the Institute of Electrical and Electronics Engineers (IEEE) Visualization and Visual Analytics (VIS) conference, held October 22-27 in Melbourne, Australia.
Northwestern Engineering’s Jessica Hullman, Matthew Kay, and several of their current and former PhD students and postdocs presented impactful research and received prestigious awards at the event, which is the premier annual forum for advances in theory, methods, and applications of visualization and visual analytics.
Hullman, Ginni Rometty Professor and associate professor of computer science at the McCormick School of Engineering, and Kay, associate professor of computer science at Northwestern Engineering and associate professor of communication studies in Northwestern’s School of Communication (SoC), codirect the MU Collective, a research lab working at the intersection of information visualization and uncertainty communication, combatting misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty in alignment with how people think.
Significant New Researcher Award
Kay received the IEEE Visualization and Graphics Technical Community Significant New Researcher Award in recognition of his work on uncertainty visualization, the design of human-centered tools for data analysis, and visualization literacy.
Working in the areas of human-computer interaction and information visualization, Kay examines problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building, and evaluating interactive systems, and designing and testing visualization techniques.
Drawing upon theories of human perception and uncertainty cognition, Kay and his team develop uncertainty visualizations that improve people's decision quality, such as his widely cited work on quantile dotplots, a discretized form of a density plot designed to communicate probabilistic forecasts. Kay also builds tools to help data analysts communicate uncertainty more effectively. He is the author of the tidybayes and ggdist R packages for visualizing Bayesian statistical model output and uncertainty, which are used by researchers and data scientists in fields including psychology, ecology, political science, and biostatistics.
Research Paper Awards
A paper by 10 Northwestern coauthors, titled “Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 US. Midterms,” earned a Best Paper Award at IEEE VIS 2023.
The team presented a longitudinal, large-scale, real-time study on the impact of uncertainty visualization of election forecasts in the US 2022 midterms. Using Bayesian multilevel modeling and post-stratification to produce demographically representative estimates, the researchers found that different election forecast visualizations can heighten emotions, increase trust, and slightly affect people's intentions to participate in elections to different degrees. Their qualitative analysis uncovers the complex political and social contexts of election forecast visualizations, underscoring that visualizations may provoke polarization.
“The study is researched, planned, and executed in an exemplary manner,” said the IEEE VIS 2023 best papers committee. “The experimental results offer intriguing insights into the effects of these uncertainty visualizations on viewer emotions, trust, and intention to vote. Beyond the topical subject, the findings are useful to any uncertainty visualization endeavors.”
Collaborators included computer science postdoctoral scholar Fumeng Yang and technology and social behavior PhD student Mandi Cai — both advised by Kay — SoC media, technology, and society PhD students Chloe Mortenson and Hoda Fakhari; former SoC postdoc Ayse Deniz Lokmanoglu (Clemson University). Faculty contributors are Hullman; Kay; Nicholas Diakopoulos, professor of communication studies in SoC and (by courtesy) professor of computer science in Northwestern Engineering; Steven Franconeri, professor of psychology in Northwestern’s Weinberg College of Arts and Sciences and (by courtesy) professor of computer science; and Erik Nisbet, Owen L. Coon Endowed Professor of Policy Analysis and Communication and SoC.
Alex Kale, a former visiting scholar in computer science at Northwestern Engineering advised by Hullman, received a VIS Doctoral Dissertation Award Honorable Mention for his PhD dissertation, “Cognitive mechanisms for reasoning with uncertainty visualizations.” Kale is currently an assistant professor of computer science at the University of Chicago.
Additional Northwestern contributions to the IEEE VIS 2023 technical program and tutorials included:
- “EVM: Incorporating Model Checking into Exploratory Visual Analysis” — Ziyang Guo, a PhD student in computer science advised by Hullman; Jeffrey Heer (University of Washington); Hullman; Kale; and Xiaoli Li Qiao (MS ’21)
- “The Rational Agent Benchmark for Data Visualization” — Jason Hartline, professor of computer science; Hullman; Guo; Michalis Mamakos, a postdoctoral scholar in psychology at Weinberg; and Yifan Wu, PhD student in computer science
- “Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research” — Hullman and Hariharan Subramonyam (Stanford University)
- “ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics” — Kay
- “Dupo: A Mixed-Initiative Authoring Tool for Responsive Visualization” — Hullman; Jane Hoffswell (Adobe); Hyeok Kim, a PhD candidate in computer science; and Ryan Rossi (Adobe Research)
- “The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance” — Karen Bonilla, Russell Davis, Yiren Ding, Mi Feng, and Lane Harrison (Worcester Polytechnic Institute); Brian D. Hall (University of Michigan); Kay; and Xiaoying Pu (University of California, Merced)
- “Adaptive Assessment of Visualization Literacy” — PhD students in computer science Yuan Cui and Lily W. Ge, PhD candidate in computer science, Ding; Harrison; Kay; and Yang
- “Choose-your-own’ D3 labs for learning to adapt online code” — Maryam Hedayati, PhD student in computer science; and Kay
- “Transparent Practices for Quantitative Empirical Research” — Hedayati; Abhraneel Sarma, PhD student in computer science; Chat Wacharamanotham (Swansea University); and Yang