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Work includes contributions expressed through five published papers, spanning radiation therapy prediction, moral framing analysis in social media, explainable data mining of spatial cohort data, spatial clustering for radiation oncology, and a visual digital twin for interventions and temporal treatment outcomes. Emphasis is placed on human-centered interface requirements, visual steering, evaluation, and clinically relevant case studies.","Visual Computing Design for Explainable, Spatially-aware Machine Learning  \nAndrew Wentzel  \nM.S., University of Illinois Chicago, 2019  \nB.Eng., The Cooper Union, 2016  \nDissertation  \nSubmitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at the Graduate College of the University of Illinois at Chicago, 2025  \nChicago, Illinois  \nDefense Committee:  \nDr. G.Elisabeta Marai, Chair and Advisor Dr. Fabio Miranda  \nDr. Xinhua Zhang  \nDr. Guadalupe Canahuate, University of Iowa Dr. Renata Raidou, TU Wien  \nCopyright Andrew George Wentzel  \n2024  \nii  \nAcknowledgments  \nI would like to thank the other members of the Electronic Visualization laboratory. I would like specifically to thank Juan Trelles Trabucco Trelles for helping to teach me most of what I know about software engineering and my fellow inmates Carla Floricel Sosea, Nafiul Nipu, and Sanjana Srabanti for their support through the years, as well as Dana and Lance Long at the EVL for their very patient logistical and technical support.  \nI would also like to thank my Advisor and the team at the MD Anderson Cancer Center and the University of Iowa, namely Guadalupe Canahuate, Xinhua Zhang, David Fuller, Abdallah S.R. Mohamed, Mohamed Naser, and Serageldin Attia, for being excellent collaborators in our work and often serving as the “humans” for the “Human-Centered Design”part of my work.  \nFinally, I would like to thank my Mother, Lisa Wentzel, my late Grandparents Patrick and Barbara Obrien, and my brothers Matt and Jake for their emotional support and willingness to drive me places. Finally, I want to thank my cat Patches for carefully supervising me when working at home.  \nContributions of Authors  \nThis thesis consists of work from 5 published papers. For all papers, G.E. Marai helped with the research direction, and the drafting and editing of the paper and conference presentation as my advisor.  \n1. Chapter 2 In “Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration” [345], the interface was based on the master’s thesis work of Peter Hanula, who designed most of the stylized 3-d radiation dose plot and controls for selecting organs and patients, as well as did some preliminary work experimenting with similarity methods; Tim Luciani helped in the design of some of the figures and editing the final paper; Baher Elgohari, and Hesham Elhalawani helped with providing the data; Guadalupe Canahuate, David Vock and Clifton David Fuller provided feedback on the interface and analysis methodology.  \n2. Chapter 3 In “MOTIV: Visual Exploration of Moral Framing in Social Media” [348], Lauren Levine and Andrew Rojecki provided annotations and moral foundation labels for the tweets; Vipul Dhariwal, Abari Bhattacharya, and Barbara Di Eugenio helped create the Twitter dataset and identify the process for finding tweet stance and relevance; and Elena Zheleva and Zahra Fatemi helped with sentiment analysis. All coauthors helped with requirements gathering and providing feedback for the interface.  \n3. Chapter 4 In “DASS Good: Explainable Data Mining of Spatial Cohort Data” [344], Carla Floricel helped with design choices on the interface, namely layout and color choice, as well as helped edit the paper; Lisanne Van Dijk helped gathr and preprocessing the patient dose-volume data; and Guadalupe Canahuate, Mohamed A Naser, Abdallah S.R. Mohamed, and C.D. fuller provided design requirements and feedback for the interface and helped with drafting the clinical paper.  \n4. Chapter 5 In “Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology” [343], all listed coauthors helped with requirements gathering and feedback,  \nas well as drafting clinical papers. Guadalupe Canahuate also helped test different clustering methods for the associated papers. The graph-based lymph node designsand dendrograms were designed by Tim Luciani.  \n5. Chapter 6 In “DITTO: A Visual Digital Twin for Inter","cbCaiaKkRW1XLtmb","https://ap.wps.com/l/cbCaiaKkRW1XLtmb","pdf",20583806,1,251,"English","en",105,"# Introduction\n## Motivation\n## Contributions\n## Background and Terminology\n### Terminology\n### Explainable ML\n### Visual Computing\n# (TSSIM) Visual Spatial Case-based Reasoning for Radiation Plan Prediction\n## Introduction\n## Related Work\n## Methods\n## Evaluation and Results","[{\"question\":\"What is the main topic of the dissertation?\",\"answer\":\"The dissertation centers on visual computing designs for explainable, spatially-aware machine learning, emphasizing interfaces that support exploration and interpretation of spatial data and models.\"},{\"question\":\"What kinds of applications are covered in the included papers?\",\"answer\":\"The work spans radiation therapy prediction and exploration, social media moral framing, explainable spatial cohort data mining, spatial clustering in radiation oncology, and a visual digital twin for interventions and temporal treatment outcomes.\"},{\"question\":\"How does the dissertation address explainability and user interaction?\",\"answer\":\"It highlights human-centered design with visual steering and interface requirements, so users can explore results and understand model-relevant behavior through thoughtfully designed visual components.\"}]","Visual Computing Design for Explainable, Spatially-aware Machine Learning - 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