CAREER: Discovering Structure in Uncertainty: Using Topology for Interactive Visualization of Uncertainty
Award Number and Duration
NSF III-2316496: February 10, 2023 to August 31, 2024
NSF III-1845204: September 1, 2019 to February 9, 2023
NSF III-2027787: REU Supplement
NSF III-2129857: REU Supplement
PI and Point of Contact
Paul Rosen
Associate Professor
Kahlert School of Computing
Scientific Computing and Imaging Institute
University of Utah
http://www.cspaul.com
paul DOT rosen AT utah DOT edu
Overview
In science, ensembles are used to model uncertainties that occur in data from a variety of sources, including errors in measurements, inaccuracies in modeling, and a lack of adequate sampling. Understanding these errors is critical to improving human understanding of phenomena in many areas of science, from urban planning to astrophysics to medicine to weather forecasting, etc. This project investigates new Topological Data Analysis and visualization methods to analyze uncertain data. This will enable scientists to better understand phenomena within their domain by developing new insights and making discoveries more quickly. The techniques will be tested in collaboration with a biomedical engineering research team helping to develop new life-saving treatments for heart attacks and a research team helping to develop technologies that support a safe, clean, and reliable national energy grid. Furthermore, this project will study and advocate for integrating better teaching methodologies, such as peer review, into computer science curricula. The results will be integrated into visualization and computational geometry courses through course materials, such as design mini-challenges, and shared with the educational community through outreach activities, such as pedagogy-themed panels and workshops.
To accomplish the goals of the project, the tools of Topological Data Analysis provide a strong theoretical basis for robustly extracting features from ensembles and designing visualizations for performing important uncertainty analysis tasks, including identifying and ranking similarities, identifying and ranking variations, and correlating topological features. This project addresses two important scientific questions: how to effectively use topology to extract features from ensembles; and how to design visualizations for domain experts that efficiently communicate the features. To extract features from an ensemble, the project will investigate new methods of robustly comparing and contrasting the topology of multiple ensemble realizations. Then, in collaboration with domain scientists, it will design new visualization methods for efficiently and effectively comparing and exploring the features and variations within ensembles. The project web site provides additional information and will include access to developed tools, data sets, and educational content.
Manuscripts
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Do You See What I See? Eliciting High-Level Visualization Comprehension |
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CLAMS: Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering |
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A Comparative Study Of The Perceptual Sensitivity Of Topological Visualizations To Feature Variations |
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Automatic Scatterplot Design Optimization For Clustering Identification |
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AffectiveTDA: Using Topological Data Analysis To Improve Analysis And Explainability In Affective Computing |
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Through The Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis Of Peer Review Text |
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A Survey Of Perception-Based Visualization Studies By Task |
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Polarity In The Classroom: An Application Leveraging Peer Sentiment Towards Scalable Assessment |
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An Efficient Data Retrieval Parallel Reeb Graph Algorithm |
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Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology |
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Theses
- Curtis Davis, Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation during Dimensionality Reduction, Oct 2021
< https://www.proquest.com/docview/2605303723 > - Ghulam Jilani Quadri, Constructing Frameworks for Task-Optimized Visualizations, Oct 2021
< https://www.proquest.com/docview/2600333411 >
🏆 2022 VGTC Best Dissertation Award - Elhamdadi, Hamza, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, July 2021
< https://www.proquest.com/docview/2563672460 > - Tanmay Kotha, Establishing Topological Data Analysis: A Comparison of Visualization Techniques, Oct 2020
< https://www.proquest.com/docview/2456448127 >
Presentations
- Paul Rosen, Topology in InfoVis: Scatterplots, Line Charts, Graphs, and Dimension Reduction, TDA in VIS Summer School, Norrkoping, Sweden (given virtually), Aug. 2023
- Paul Rosen, Improving Visualization Through Shape–A Discussion on Perception, Confidence, and Trust, Tufts University, Medford, MA, Apr. 2023
- Paul Rosen, Perception, Confidence, and Trust in Large Data Visualization, Computer Graphics Forum, Idaho Fall, IA, Apr. 2023
- Paul Rosen, Improving Visualization Through ShapeA Discussion of Topology-based Methods in Visualization, Campus Alliance for Advanced Visualization (CAAV) Seminar Series, Virtual, Mar. 2023
- Paul Rosen, Through the Looking Glass: Insights into Visualization Pedagogy through Sentiment Analysis of Peer Review Text, IEEE VIS, October 2022
- Ghulam Jilani Quadri, A Survey of Perception-Based Visualization Studies by Task, IEEE VIS, October 2022
- Md Dilshadur Rahman, A Qualitative Evaluation and Taxonomy of Student Annotations on Bar Charts, VisComm Workshop @ IEEE VIS, October 2022
- Bhavana Doppalapudi, Untangling Force-Directed Layouts Using Persistent Homology, TopoInVis Workshop @ IEEE VIS, October 2022
- Paul Rosen, Taming the Uncertainty Induced During Data Transformation and Visual Encoding, Dagstuhl Seminar 22331 (Virtual), August 2022
- Paul Rosen, Optimizing and Interacting with Information Visualizations Using Topological Data Analysis, Topological Data Visualization Workshop, May 2022
- Paul Rosen, Improving the Visualization of Large and Complex Data Through Topology-based Design and Interaction, University of Utah, February 2022
- Hamza Elhamdadi, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, IEEE VIS, October 2021
- Ghulam Jilani Quadri, Modeling the Perception for Effective Visualization, VIS Summer Camp Student Seminar Series, Virtual, May 2021
- Hamza Elhamdadi, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, VIS Virtual Summer Camp Student Seminar Series, June 2021
- Paul Rosen, LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts, IEEE VAST, October 2020
- Ghulam Jilani Quadri, Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology, IEEE InfoVis, October 2020
- Mustafa Hajij, Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology, IDSTA, October 2020
- Zachariah Beasley, Leveraging Peer Feedback to Improve Visualization Education, PacificVis, June 2020
- Ashley Suh, TopoLines: Topological Smoothing for Line Charts, EuroVis, May 2020.
- Brad Hollister, Visual Inspection of DBS Efficacy, IEEE VIS Short Paper Track, October 2019
- Ghulam Jilani Quadri, You Can’t Publish Replication Studies (and How to Anyways): Position Paper, Vis X Vision, October 2019
Software
AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
< https://github.com/USFDataVisualization/AffectiveTDA >
A Survey of Perception-Based Visualization Studies by Task
< https://usfdatavisualization.github.io/VisPerceptionSurvey/ >
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
< https://github.com/USFDataVisualization/LineSmooth >
< https://usfdatavisualization.github.io/LineSmoothDemo/ >
Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology
< https://github.com/USFDataVisualization/TopoClusterPerception >
< https://usfdatavisualization.github.io/TopoClusterPerceptionDemo/ >
TopoLines: Topological Smoothing for Line Charts
< https://github.com/USFDataVisualization/TopoLines >
< https://usfdatavisualization.github.io/TopoLines/ >
Personnel
Sefat Rahman (Graduate RA, Jan 2023-current)
Scientific Computing and Imaging Institute
University of Utah
sefat DOT rahman AT utah DOT edu
Former Personnel
Bhavana Doppalapudi (Graduate RA, Jan 2020-July 2022)
Computer Science & Engineering
University of South Florida
bdoppalapudi AT usf DOT edu
Keshon Primus (Undergrad RA, May 2022-July 2022)
Computer Science & Engineering
University of South Florida
keshonprimus AT usf DOT edu
Subsequent Position: Student at University of South Florida
Francis Hahn (REU, May 2020-May 2022)
Computer Science & Engineering
University of South Florida
fhahn AT usf DOT edu
Subsequent Position: Graduate student at University of South Florida
Bryan Triana (REU, Sept 2021-May 2022)
Computer Science and Engineering
University of South Florida
bryantriana AT usf DOT edu
Subsequent Position: Software Engineer at Google
Ghulam Jilani Quadri, PhD, Dec 2021
Graduate RA, Aug 2020 – Dec 2021
Computer Science & Engineering
University of South Florida
ghulamjilani AT usf DOT edu
Subsequent Position: CI Fellows Postdoc at UNC Chapel Hill
Hamza Elhamdadi, MS Aug 2021
Graduate RA, Aug 2020 – Aug 2021
Computer Science & Engineering
University of South Florida
hme1 AT usf DOT edu
Subsequent Position: PhD Student at UMass Amherst
Raquel Garcia, BS
REU, May 2020-May 2021
Computer Science & Engineering
University of South Florida
raquelgarcia AT usf DOT edu
Subsequent Position: Software Engineer at Microsoft
Tanmay Kotha, MS, Dec 2020
Graduate RA, Aug 2019 – July 2020
Computer Science & Engineering
University of South Florida
tanmay AT usf DOT edu
Subsequent Position: Software Engineering at Amazon
Curtis Davis, BS/MS, Dec 2021
REU, May 2020-Dec 2020
Computer Science & Engineering
University of South Florida
ctd AT usf DOT edu
Subsequent Position: Masters student at the University of South Florida
Collin Chimbwanda, BS
REU, May 2020-Dec 2020
Computer Science & Engineering
University of South Florida
cchimbwanda AT usf DOT edu
Collaborators
Alon Friedman
Chris R. Johnson
Zachariah Beasley
Ashley Suh
Shaun Canavan
Mustafa Hajij
Junyi Tu
Tushar Athawale
Dave Pugmire
Acknowledgments
This material is based upon work supported or partially supported by the National Science Foundation under Grant No. 2316496, project titled “CAREER: Discovering Structure in Uncertainty: Using Topology for Interactive Visualization of Uncertainty”.
Any opinions, findings, and conclusions or recommendations expressed in this project are those of author(s) and do not necessarily reflect the views of the National Science Foundation.
Last update October 4, 2022
