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Documents authored by Rozier, Kristin Yvonne

Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.24
Understanding Time in Space: Improving Timeline Understandability for Uncrewed Space Systems

Authors: Elizabeth Sloan and Kristin Yvonne Rozier

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
Timelines are critical in space exploration. Timelines facilitate planning, resource management, and automation of uncrewed missions. As NASA and other space agencies increasingly rely on timelines for autonomous spacecraft operations, ensuring their understandability and verifiability is essential for mission success. However, interdisciplinary design teams face challenges in interpreting timelines due to variations in cultural and educational backgrounds, leading to communication barriers and potential system mismatches. This work-in-progress research explores time-oriented data visualizations to improve timeline comprehension in space systems. We contribute (1) a survey of visualization techniques, identifying patterns and gaps in historic time-oriented data visualizations and industry tools, (2) a focus group pilot study analyzing user interpretations of timeline visualizations, and (3) a novel method for visualizing aggregate runs of a timeline on a complex system, including identification of key features for usability of aggregate-data visuals. Our findings inform future visualization strategies for debugging and verifying timelines in uncrewed systems. While focused on space, this research has broader implications for aerospace, robotics, and emergency response systems.
Cite as

Elizabeth Sloan and Kristin Yvonne Rozier. Understanding Time in Space: Improving Timeline Understandability for Uncrewed Space Systems. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 24:1-24:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sloan_et_al:OASIcs.SpaceCHI.2025.24,
  author =	{Sloan, Elizabeth and Rozier, Kristin Yvonne},
  title =	{{Understanding Time in Space: Improving Timeline Understandability for Uncrewed Space Systems}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{24:1--24:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.24},
  URN =		{urn:nbn:de:0030-drops-240143},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.24},
  annote =	{Keywords: Human-Ceneterd Design, Time-Oriented Data Visualization, Uncrewed Spacecraft Operations, Formal Methods}
}
Document
DOI: 10.4230/DagRep.13.12.24
Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492)

Authors: Ellen Enkel, Nils Jansen, Mohammad Reza Mousavi, and Kristin Yvonne Rozier

Published in: Dagstuhl Reports, Volume 13, Issue 12 (2024)

Abstract
The term of a model has different meanings in different communities, e.g., in psychology, computer science, and human-computer interaction, among others. Well-defined models and specifications are the bottleneck of rigorous analysis techniques in practice: they are often non-existent or outdated. The constructed models capture various aspects of system behaviours, which are inherently heterogeneous in nature in contemporary autonomous systems. Once these models are in place, they can be used to address further challenges concerning autonomous systems, such as validation and verification, transparency and trust, and explanation. The seminar brought together the best experts in a diverse range of disciplines such as artificial intelligence, formal methods, psychology, software and systems engineering, and human-computer interaction as well as others dealing with autonomous systems. The goal was to consolidate these understanding of models in order to address three grand challenges in trustworthiness and trust: (1) understanding and analysing the dynamic relationship of trustworthiness and trust, (2) the understanding of mental modes and trust, and (3) rigorous and model-based measures for trustworthiness and calibrated trust.
Cite as

Ellen Enkel, Nils Jansen, Mohammad Reza Mousavi, and Kristin Yvonne Rozier. Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492). In Dagstuhl Reports, Volume 13, Issue 12, pp. 24-47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{enkel_et_al:DagRep.13.12.24,
  author =	{Enkel, Ellen and Jansen, Nils and Mousavi, Mohammad Reza and Rozier, Kristin Yvonne},
  title =	{{Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492)}},
  pages =	{24--47},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{12},
  editor =	{Enkel, Ellen and Jansen, Nils and Mousavi, Mohammad Reza and Rozier, Kristin Yvonne},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12.24},
  URN =		{urn:nbn:de:0030-drops-198543},
  doi =		{10.4230/DagRep.13.12.24},
  annote =	{Keywords: artificial intelligence, automata learning, autonomous systems, cyber-physical systems, formal methods, machine learning, safety, safety-critical systems, self-adaptive systems, software evolution, technology acceptance, trust}
}
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