Daniel Kerrigan
Research
I am interested in visualization and HCI, particularly as they relate to the development, evaluation, and use of machine learning models.
In progress
SAEfarer
SAEfarer is a visual analytics tool for exploring sparse autoencoders.
PDPilot
PDPilot is an application for guiding users in analyzing machine learning models through PDP and ICE plots.
Monomoy
Monomoy is a system for helping domain experts familiarize themselves with and identify unintuitive behavior in machine learning models.
Publications
Towards a Visual Perception-Based Analysis of Clustering Quality Metrics
Graziano Blasilli, Daniel Kerrigan, Enrico Bertini, Giuseppe Santucci
Visualization in Data Science (VDS at IEEE VIS), 2024
Measuring wake deflection from SCADA data during wake steering using machine learning
Nathan Post, Cheng Zheng, Daniel Kerrigan, Enrico Bertini, Melanie Tory
Journal of Physics: Conference Series, 2024
SliceLens: Guided Exploration of Machine Learning Datasets
Daniel Kerrigan, Enrico Bertini
In Proceedings of the Workshop on Human-In-the-Loop Data Analytics (HILDA '23), 2023
A Survey of Domain Knowledge Elicitation in Applied Machine Learning
Daniel Kerrigan, Jessica Hullman, Enrico Bertini
Multimodal Technologies and Interaction, 2021
Nutrition Bytes: Visualizing Food Content
Shuai He, Daniel Kerrigan, Ronald Metoyer
IEEE VIS Poster Extended Abstract, 2017