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About the speaker
Jessica Hullman is an Associate Professor of Computer Science at Northwestern University. Her research looks at how to design, evaluate, coordinate, & think about representations of data for amplifying cognition & decision making. She co-directs the Midwest Uncertainty Collective, a lab devoted to better representations, evaluations, & theory around data interfaces in data, with Matt Kay. Jessica is the recipient of a Microsoft Faculty Fellowship, NSF CAREER Award, & multiple best papers at top visualization & human-computer interaction conferences.
Some of the talk will cover ideas in this paper with Andrew Gelman: https://arxiv.org/abs/2104.02015
Research & development in computer science & statistics have produced increasingly sophisticated software interfaces for interactive & exploratory analysis, optimized for easy pattern finding & data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently model-free & cannot be formalized. I will motivate how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives & representations of uncertainty that can discourage users from drawing valid inferences. I will discuss how the concept of a model check in a Bayesian statistical framework unites exploratory & confirmatory analysis, & how this understanding relates to other proposed theories of graphical inference. Viewing interactive analysis as driven by model checks suggests new directions for software & empirical research around exploratory & visual analysis. More broadly, I will motivate formalization of the role of graphics in statistical practice as a counterpoint to relying on intuition or brute force empiricism to guide research & practice.