Scholar
Madeleine Grunde-McLaughlin
Google Scholar ID: wzqKsd4AAAAJ
University of Washington
Citations & Impact
Citations
714
H-index
8
i10-index
8
Publications
13
Co-authors
10
list available
Publications
Resume (English only)
Academic Achievements
- How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study, ACM Conference on Human Computer Interaction, 2024
- Explanations can Reduce Overreliance on AI Systems during Decision-Making, ACM Conference on Computer-Supported Cooperative Work and Social Computing, 2023 (Best paper honorable mention awarded to the top 23 papers)
- Measuring Compositional Consistency for Video Question Answering, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
- AGQA: A Compositional Benchmark for Spatio-Temporal Reasoning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021
- Bayesian-Assisted Inference from Visualized Data, IEEE InfoVis 2020
- Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows, ArXiv, 2023
- Semantic Navigator: Query Driven Active Learning for Historical Narrative Understanding, ACM Conference on Computer-Supported Cooperative Work and Social Computing, Community-Driven AI Workshop, 2023
- When do XAI Methods Work? A Cost-Benefit Approach to Human-AI Collaboration, TRAIT Workshop at ACM Conference on Human Computer Interaction, 2022
- AGQA 2.0: An updated benchmark for compositional spatio-temporal reasoning, ArXiv, 2022
- Model Comparison of the Effects of Stimulus Structure on Visual Working Memory Recall, Honors Thesis in Cognitive Science, Recipient of the College Alumni Society Prize in Cognitive Science, 2021
Research Experience
- Fourth-year Ph.D. student, working in the intersection of HCI and AI.
Education
- Ph.D. in Computer Science at the University of Washington, advised by Jeffrey Heer and Daniel Weld, with close collaboration with Ranjay Krishna.
Background
- PhD student in Computer Science, focusing on the intersection of HCI and AI, particularly using AI for data-driven discovery while incorporating human judgment. Her research questions include how to build validation and user guidance into AI-backed tools, how AI chaining architectures can support meaningful user interactions, and how these tools can help scientists and data analysts discover robust and reproducible findings. She is especially interested in environmental applications.
Miscellany
- Personal interests not mentioned
