PhD candidate in Information Science at the University of Washington, advised by Jessica Hullman.
Currently, a visiting scholar at Northwestern University
Data visualization, uncertainty communication, visual & statistical reasoning, HCI.
Midwest Uncertainty Collective
Interactive Data Lab
Masters of Science in Information Science, UW
Bachelors of Science in Psychology, UW
My research combines graphical perception experiments and systems design to investigate uncertainty communication. I am interested in how data visualizations are used to communicate probability and uncertainty information to non-expert audiences, especially when people rely on that information to make incentivised decisions. I am also interested in how such visualizations are incorportated into interactive systems for data analysis and decision support. As a case study on uncertainty visualization in analysis systems, I am designing software with Stottler Henke and Associates to help scientists conduct meta-analysis and communicate evidence-based recommendations to decision-making officials and other stakeholders.
Here are some representative publications (see my CV for a full list).
Visual Reasoning Strategies and Satisficing: How Uncertainty
Visualization Design Impacts Effect Size Judgments and Decisions
VIS 2020
Alex Kale, Matthew Kay, and Jessica Hullman
Boba: Authoring and Visualizing Multiverse Analyses
VIS 2020
Yang Liu, Alex Kale, Tim Althoff, and Jeffrey Heer
Adaptation and Learning Priors in Visual Inference
Position Paper
VIS 2019
Alex Kale and Jessica Hullman
Decision-Making Under Uncertainty in Research
Synthesis: Designing for the
Garden of Forking Paths
CHI 2019
Alex Kale, Matthew Kay, and Jessica Hullman
Capture & Analysis of Active Reading Behaviors for Interactive Articles on the
Web
EuroVis 2019
Matt Conlen, Alex Kale, and Jeffrey Heer
Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous
Data
InfoVis 2018
Alex Kale, Francis Nguyen, Matthew Kay and Jessica Hullman
In Pursuit of Error: A Survey of Uncertainty Visualization
Evaluation
InfoVis 2018
Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, and Matthew Kay
These are my recent talks in addition those accompanying conference papers.
SDSS 2020 - User Testing Statistical Graphics
With the proliferation of data-driven products and media in public life, conventional statistical graphics tend to emphasize point
estimates and omit uncertainty information. However, given that research in visualization, psychology, and behavioral economics
shows that people often satisfice (i.e., use heuristics that deviate from the optimal strategy) when reasoning with uncertainty,
users of data visualizations may not recognize or correctly interpret uncertainty. I argue that we need to understand users’ potential
reasoning strategies in order to design graphical interfaces that steer users toward more systematic ways of reasoning with uncertainty.
In this talk, I present empirical evidence on how users satisfice, both when reading individual charts and when conducting analysis, and
I discuss ways we are designing statistical graphics and interfaces for data analysis that anticipate users’ tendency to satisfice.
As a PhD student at the University of Washington Information School, I have been a teaching assistant for two classes, Introduction to Data Science and Interactive Data Visualization. I also spent one year leading the Computer Science and Engineering Department's graduate seminar on human computer interaction.
I helped teach Introduction to Data Science (INFO 180) with Jevin West. The course was an experimental flipped classroom in which students progressed through the course in a gameified web application created by Bob Boiko. A student's objective was to pass a set of 7 challenges by the end of the quarter. Taking these challenges required students to interact with their two TAs. I held ten office hours per week, in addition to class time, providing one-on-one and group instruction on programming in Python and introductory topics in data science and statistics. In course evaluations and in person, students have told me how the one-on-one instruction in office hours helped them to grasp concepts they were stuggling with, learn from their mistakes, and apply the knowledge and skills we built together in future endeavors. Teaching this course was a transformative experience and left me with questions about how to scale and adapt this unothodox but rewarding approach to teaching.
I also helped to teach Interactive Data Visualization (INFO 474) with Yea-Seul Kim. The course consisted of a series of lectures, in-class activities, and projects aimed at teaching students the theoretical and empirical foundations of visualization design and prompting students to put this knowledge into practice. My role as a TA was primarily to develop and execute grading schemes that balanced learning objectives with the needs of our students. I also held office hours where I helped students mock-up their projects, reason through implementation using psuedo-code, and refine their understanding of difficult concepts from lectures and readings.
I've given invited lectures on perception, color vision, and uncertainty visualization in both the UW Computer Science and Engineering department's Data Visualization course (CSE 442) and the Information School's Interactive Data Visualization course (INFO 474).
In academic year 2019-2020, I co-led the UW Computer Science and Engineering department's graduate-level Interactive Systems Seminar (CSE 590H) with Jasper Tran O'Leary under the supervision of James Fogarty. Each week we read and discussed a paper relating to human computer interaction. Our discussions focused on design principles and requirements of different systems, empirical evaluation, and critique and synthesis of the literature. My role was to organize and facilitate seminar, as well as being a regular discussion leader.
Prior to graduate school I was also an undergraduate TA for a series of statistics courses in the UW Psychology Department taught by Laura Little.