[Ieee_vis] Submission Deadline Sunday: HICSS Interactive Visual Analytics for Knowledge Integration and Decision Intelligence Minitrack

[Ebert, David - (ebertd)]

Please consider submitting your visualization and visual analytics work to our minitrack at HICSS! Also, share with others who might be interested.

Thanks,

David, Kelly, Brian

Dr. David S. Ebert
Chief AI And Data Science Officer
Associate Vice President of Research and Partnerships
Computer Science Engineering Endowed Innovation Chair, Dept. of ECE
University of Arizona
Tucson, AZ
ebertd at arizona.edu<mailto:ebertd at arizona.edu>



Interactive Visual Analytics for Knowledge Integration and Decision Intelligence Minitrack

Decision Analytics and Service Science Track



https://hicss.hawaii.edu/tracks-59/decision-analytics-and-service-science/#interactive-visual-analytics-for-ai-knowledge-integration-and-decision-intelligence-minitrack

Paper due June 15, 2025

January 6-9, 2026

Hyatt Regency Maui

https://hicss.hawaii.edu/authors/



Interactive Visual Analytics for AI, Knowledge Integration, and Decision Intelligence supports human decision making through human-computer teaming, visual interaction with AI (e.g., ML, LLM), data, and statistical and machine learning processes, with applications in a broad range of situations where human expertise must be brought to bear on problems characterized by complex, massive, and often with mis/disinformation, relevance, uncertainty, and risk aspects.

This minitrack seeks submissions that focus on the above topics and the core issues of theory and methods for visualization, visual analytics, understandable AI, knowledge integration and decision intelligence in organizations. Case studies of applications of these methods to new analytic and decision making tasks in science and technology, public health, business intelligence, financial analysis, social sciences, communications, public policy, and other domains are particularly welcome. Submissions may include studies of visual analytics and decision support in the context of an organization (e.g., communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, Interactive Machine Learning, Large Language Models, human-computer teaming, and collaborative analysis using visual information systems. Additionally, submissions may include understandable, trustable AI as well as human-guided AI to enable the problem-solving process.

For HICSS-59, the focus of the minitrack extends to multidisciplinary collaboration and communication among researchers from a variety of research perspectives and increase the focus on AI incorporation, understandable AI, and human-computer teaming. Authors are encouraged to bring the lens of their own background and expertise to focus on the analytics of the data itself and coordination of multiple levels of analysis, decision-making, communication, and operations to the design and evaluation of effective presentations for stakeholders and dissemination of trustful and actionable information. We invite computational, cognitive, communication, and organizational perspectives on advanced data processing and interactive visualization for analysis and decision-making across a range of human endeavors. We also invite participation from researchers who are looking at scaling issues and multiscale issues, whether these scales refer to the time of decision making, the form-factor and operational constraints of mobile devices, the number of decision makers or the more traditional notion of multiscale simulation and real-world scales of data. We are particularly interested in approaches that combine computational and interactive analytics in "mixed initiative" or Interactive Machine Learning systems, decision support in the context of an organization (e.g. communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, and collaborative analysis using visual information systems, including developing trustable AI and the challenge of dis/misinformation.

Minitrack Co-Chairs:

David S. Ebert

Chief AI And Data Science Officer

Associate Vice President of Research and Partnerships

Computer Science Engineering Endowed Innovation Chair, Dept. of ECE

University of Arizona

ebertd at arizona.edu<mailto:ebertd at arizona.edu>



Kelly Gaither

University of Texas at Austin

kelly at tacc.utexas.edu<mailto:kelly at tacc.utexas.edu>

Brian Fisher

Simon Fraser University

bfisher at sfu.ca<mailto:bfisher at sfu.ca>



Dr. David S. Ebert
Chief AI And Data Science Officer
Associate Vice President of Research and Partnerships
Computer Science Engineering Endowed Innovation Chair, Dept. of ECE
University of Arizona
Tucson, AZ
ebertd at arizona.edu<mailto:ebertd at arizona.edu>

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