Alex Kale

· Assistant Professor of Computer Science

University of Chicago · Computer Science

Active 2016–2025

h-index14

Citations1.0k

Papers4623 last 5y

Funding—

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About

Alex Kale is an Assistant Professor of Computer Science and Data Science at the University of Chicago. His research focuses on advancing the field of computer science through contributions to data science and related areas. As part of the faculty, he is involved in exploring interdisciplinary applications within computer science, contributing to the department's mission of defining and building the future of the field from theory to applications and from science to society.

Research topics

Computer Science
Software engineering
Programming language
Data science
Mathematics
Epistemology
Psychology
Econometrics
Social psychology
Statistics

Selected publications

multiverse: Multiplexing Alternative Data Analyses in R Notebooks
2023-04-19 · 21 citations
article
There are myriad ways to analyse a dataset. But which one to trust? In the face of such uncertainty, analysts may adopt multiverse analysis: running all reasonable analyses on the dataset. Yet this is cognitively and technically difficult with existing tools—how does one specify and execute all combinations of reasonable analyses of a dataset?—and often requires discarding existing workflows. We present multiverse, a tool for implementing multiverse analyses in R with expressive syntax supportin…
Publisher DOI
EVM: Incorporating Model Checking into Exploratory Visual Analysis
IEEE Transactions on Visualization and Computer Graphics · 2023-01-01 · 15 citations
article1st authorCorresponding
Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data-e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the…
Publisher DOI
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
arXiv (Cornell University) · 2021-12-06 · 14 citations
preprintOpen access
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment. However, it is unclear how we can fix these models. We present our ongoing work, GAM Changer, an open-source interactive system to help data scientists and domain experts easily and responsibly edit their Generalized Additive Models (GAMs). With novel visualization techniques, our tool puts interpretability i…
Publisher OA PDF DOI
MetaExplorer : Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis
2023-04-19 · 8 citations
preprintOpen access1st authorCorresponding
Scientists often use meta-analysis to characterize the impact of an intervention on some outcome of interest across a body of literature. However, threats to the utility and validity of meta-analytic estimates arise when scientists average over potentially important variations in context like different research designs. Uncertainty about quality and commensurability of evidence casts doubt on results from meta-analysis, yet existing software tools for meta-analysis do not provide an explicit sof…
Publisher OA PDF DOI
Underreporting of AI Use: The Role of Social Desirability Bias
SSRN Electronic Journal · 2025-01-01 · 3 citations
preprintOpen access
Publisher DOI

Frequent coauthors

Jessica Hullman
Northwestern University
20 shared
Michael‐Paul Schallmo
University of Minnesota
20 shared
Scott O. Murray
University of Washington
19 shared
Raphael Bernier
University of Washington
19 shared
Rachel Millin
Bellevue Hospital Center
18 shared
Anastasia V. Flevaris
University of Washington
13 shared
Matthew Kay
Northwestern University
13 shared
Tamar Kolodny
12 shared

Education

Ph.D., Information Science
University of Washington
2022
M.S., Information Science
University of Washington
2020
B.S., Psychology, with minors in Music and Philosophy
University of Washington
2015

Awards & honors

2026 NSF Early CAREER Award
2023 Best paper honorable mention, CHI
2021 Best paper honorable mention, VIS
2020 Best paper, VIS

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