| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | February 3, 2015 |
| Latest Amendment Date: | March 5, 2024 |
| Award Number: | 1452977 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Dan Cosley
dcosley@nsf.gov (703)292-8832 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2015 |
| End Date: | April 30, 2025 (Estimated) |
| Total Intended Award Amount: | $499,948.00 |
| Total Awarded Amount to Date: | $553,876.00 |
| Funds Obligated to Date: |
FY 2016 = $96,554.00 FY 2017 = $105,906.00 FY 2018 = $119,540.00 FY 2019 = $122,619.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
80 GEORGE ST MEDFORD MA US 02155-5519 (617)627-3696 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
161 College Ave Medford MA US 02155-5807 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
HCC-Human-Centered Computing, Other Global Learning & Trng |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Visual analytics systems combine human analysis with computational techniques. When the two are fully integrated these next generation human-in-the-loop systems can be tremendously powerful, but current visual analytics systems suffer from a significant communication gap between the human and the computer which prevents them from reaching their full potential. Specifically, there currently exist no generalized techniques to enable the computer to easily and intuitively understand the user's background knowledge, analysis process, or intent during an analysis task. Lacking this, the computer has limited means to identify the user's needs and provide timely and appropriate computational support. The PI's goal in this project is to develop computational techniques to quantify and extract the user's high-level knowledge by analyzing his/her interactions with a visual interface. Toward this end, the research agenda consists of two complementary components: data modeling and user modeling. In data modeling, the user's interactions are used to learn the parameters of an algorithm, computational process, or representation of the data that best reflect the user's domain knowledge about the data. In user modeling, the same interactions are used to infer aspects of the user's reasoning process and cognitive style. Together, the results of these two modeling processes will give the computer the means to better understand the user's analysis process and will enable it to better support the user in performing his/her task.
The results of this work will have both immediate and long term impact on the research and application of visual analytics. In the short term new techniques for data and user modeling will advance existing systems and practices in interactive data analysis, while in the long term project outcomes will help establish the foundations of a human+computer approach to visual analytics that more effectively supports the user's analysis process, which in turn will impact the development of real-time, mixed-initiative visual analytics systems for addressing the challenges of big data. In addition, through an integrated research and education agenda, students will be trained with the appropriate balance of expertise in human reasoning and computational science, preparing them to conduct independent interdisciplinary research and lead future efforts in visual analytics.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This CAREER project, "Analyzing Interactions in Visual Analytics for User and Data Modeling," has significantly advanced the state of knowledge in both data modeling and user modeling within the domain of human-centered visual analytics. Over the ten-year period, we developed novel theoretical frameworks, built interactive systems, conducted human-subject experiments, and produced peer-reviewed publications that have shaped the understanding of how users interact with complex data systems.
Major Contributions
User Modeling:
The early phase of the project focused on understanding users' cognitive styles, reasoning processes, and interaction behaviors. We developed models that predict user interaction phases and actions in visual analytics tasks, with early successes resulting in a SIGMOD publication in 2016. Subsequent work explored adaptive systems for learning (e.g., piano instruction using brain-sensing technology) and medical decision support systems to improve patient understanding of risk, a project that culminated in a best paper award at CHI 2016 and multiple IEEE VIS publications.
Data Modeling:
In the later years, we shifted focus to the data modeling aspect, including a predicate-based framework for interpretable data characterization. These first-order predicates describe and explain subsets of data with logical precision. This framework was applied to multiple domains: anomaly detection, high-dimensional data projection interpretation, and graph visualization recommendation. Most recent work on this framework was accepted to IEEE VIS 2024.
Foundational Theories and Evaluation Methods:
A significant theoretical contribution was the development of a formal hypothesis grammar to model analytic reasoning tasks. This grammar defines hypothesis structures based on variables, relations, and evaluative methods. It inspired the creation of inferential tasks as a novel evaluation methodology bridging low-level (e.g., fact-finding) and high-level (e.g., insight-based) visualization assessments. This line of research was recognized with a Best Short Paper Award at EuroVis 2022.
Impact
This project has contributed foundational insights to visual analytics, human-computer interaction (HCI), and explainable AI. The predicate framework offers a language for articulating data features that matter most to human analysts, improving transparency and trust. The hypothesis grammar and inferential task methodologies are already influencing how we evaluate and teach visual analytics.
The broader research community has acknowledged the impact through multiple high-profile publications and awards. Several tools and frameworks developed in this project have informed follow-on NSF- and DARPA-funded initiatives.
Human Resource Development
The project supported numerous students across all levels. Four PhD students completed their dissertations during this project, including Brian Montambault (defending in 2025), Ashley Suh (2023), Marianne Procopio (2020), Beste Yuksel (2016). Multiple undergraduates participated through REU supplements, many of whom pursued graduate study or research careers thereafter. The mentoring structure of this project has helped cultivate the next generation of researchers in visual analytics and HCI.
Broader Impacts and Dissemination
Results were disseminated through more than 30 peer-reviewed publications, keynote talks, and invited presentations. Tools and techniques developed have been integrated into graduate and undergraduate curricula at Tufts University, notably in visual analytics coursework.
Last Modified: 07/01/2025
Modified by: Remco Chang
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