III: Medium: Collaborative Research: |
Award Number and Duration |
NSF IIS 1513616: September 1, 2015 to August 31, 2019 plus 1-year NCE |
Point of Contact |
Bei Wang |
PI and Co-PIs |
Bei Wang (PI) |
Carlos Scheidegger (Co-PI) |
Paul Rosen (Co-PI) |
Overview |
This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically. |
Honors and Awards |
Finalist in the Computing Research Association (CRA) Outstanding Undergraduate Researchers, Nithin Chalapathi (REU, Undergraduate RA), 2021.
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Publications |
Papers marked with * use alphabetic ordering of authors. Students are underlined. |
Year 5 (2019 - 2020) | |
TopoAct: Visually Exploring the Shape of Activations in Deep Learning. Archit Rathore, Nithin Chalapathi, Sourabh Palande, Bei Wang. Computer Graphics Forum, accepted, 2021. Supplemental Material. arXiv:1912.06332. | |
Uncertainty Visualization of 2D Morse Complex Ensembles Using
Statistical Summary Maps.
Tushar Athawale, Dan Maljovec, Lin Yan, Chris R. Johnson, Valerio Pascucci, Bei Wang. IEEE Transactions on Visualization and Computer Graphics, 2020. DOI: 10.1109/TVCG.2020.3022359 | |
Probabilistic Convergence and Stability of Random Mapper Graphs.
Adam Brown, Omer Bobrowski, Elizabeth Munch, Bei Wang. Journal of Applied and Computational Topology, 2020. DOI: 10.1007/s41468-020-00063-x arXiv:1909.03488. | |
Sheaf-Theoretic Stratification Learning From Geometric and Topological Perspectives.
Adam Brown and Bei Wang. Discrete & Computational Geometry, 2020. DOI:10.1007/s00454-020-00206-y arXiv:1712.07734 | |
Moduli Spaces of Morse Functions for Persistence.
Michael J. Catanzaro, Justin Curry, Brittany Terese Fasy, Janis Lazovskis, Greg Malen, Hans Riess, Bei Wang, Matthew Zabka. Journal of Applied and Computational Topology, 4, pages 353-385, 2020. DOI:10.1007/s41468-020-00055-x arXiv:1909.10623. | |
Spatio-Temporal Visualization of Interdependent Battery Bus Transit and Power Distribution Systems.
Avishan Bagherinezhad, Michael Young, Bei Wang, Masood Parvania. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), accepted, 2021. | |
Interactive Visualization of Interdependent Power and Water Infrastructure Operation. Han Han, Konstantinos Oikonomou, Nithin Chalapathi, Masood Parvania, Bei Wang. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020. DOI:10.1109/ISGT45199.2020.9087680 | |
Spectral Sparsification of Simplicial Complexes for Clustering and Label Propagation.
Braxton Osting, Sourabh Palande and Bei Wang. Journal of Computational Geometry, 11(1), pages 176-211, 2020. Online: Journal of Computational Geometry. arXiv:1708.08436. | |
On Homotopy Types of Vietoris--Rips Complexes of Metric Gluings.
Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Journal of Applied and Computational Topology, 4, pages 425-454, 2020. DOI:10.1007/s41468-020-00054-y arXiv:1712.06224. | |
Mathematical Foundations in Visualization.
Ingrid Hotz, Roxana Bujack, Christoph Garth, Bei Wang. In Foundations of Data Visualization, Springer, 2020 Editors: Min Chen, Helwig Hauser, Penny Rheingans, Gerik Scheuermann. DOI:10.1007/978-3-030-34444-3 | |
Topological Inference of Manifolds with Boundary.
Yuan Wang, Bei Wang. Computational Geometry: Theory and Applications, 88(101606), 2020. DOI:10.1016/j.comgeo.2019.101606 arXiv:1810.05759 | |
Visual Demo of Discrete Stratified Morse Theory (Media Exposition)
Youjia Zhou, Kevin Knudson, Bei Wang. International Symposium on Computational Geometry (SoCG), 2020. DOI: 10.4230/LIPIcs.SoCG.2020.82 | |
An Efficient Data Retrieval Parallel Reeb Graph Algorithm. Mustafa Hajij, Paul Rosen MDPI Algorithms, 13(10), pages 258, 2020. DOI:10.3390/a13100258 | |
TopoLines: Topological Smoothing for Line Charts. Paul Rosen, Ashley Suh, Christopher Salgado, Mustafa Hajij Eurographics Conference on Visualization (EuroVis) Short Papers, 2020. DOI:10.2312/evs.20201053 arXiv:1906.09457 | |
Parallel Mapper. Mustafa Hajij, Basem Assiri, Paul Rosen Proceedings of the Future Technologies Conference, pages 717 -731, 2020. DOI:10.1007/978-3-030-63089-8_47 arXiv:1712.03660 | |
The Relationship Between the Intrinsic Cech and Persistence Distortion Distances for Metric Graphs.
Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Journal of Computational Geometry, 10(1), pages 477-499, 2019. DOI: 10.20382/jocg.v10i1a16 arXiv:1812.05282 | |
Year 4 (2018 - 2019) | |
Persistent Homology Guided Force-Directed Graph Layouts.
Ashley Suh, Mustafa Hajij, Bei Wang, Carlos Scheidegger, Paul Rosen IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of InfoVis), 26(1), pages 697-707, 2020. DOI: 10.1109/TVCG.2019.2934802 arXiv:1712.05548 Long Video. Short Video. |
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A Structural Average of Labeled Merge Trees for Uncertainty Visualization.
Lin Yan, Yusu Wang, Elizabeth Munch, Ellen Gasparovic, Bei Wang. IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of SciVis), 26(1), pages 832-842, 2020. Supplemental Material. Doi: 10.1109/TVCG.2019.2934242 arXiv:1908.00113 Long Video. Short Video. |
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A Kernel for Multi-Parameter Persistent Homology.
René Corbet, Ulderico Fugacci, Michael Kerber, Claudia Landi, Bei Wang. Shape Modeling International (SMI), 2019. Computers & Graphics: X, 2, 100005, 2019. DOI:10.1016/j.cagx.2019.100005 arXiv:1809.10231 Best Paper Award at SMI 2019! | |
Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.
Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher and Bei Wang. International Workshop on Connectomics in NeuroImaging (CNI) at MICCAI, 2017. Brain Connectivity, 9(1):13-21, 2019 DOI: 10.1089/brain.2018.0604 |
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Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology. Jochen Jankowai, Bei Wang, Ingrid Hotz. Eurographics Conference on Visualization (EuroVis), 2019. Computer Graphics Forum (CGF), 38(3), pages 337-349, 2019. DOI:10.1111/cgf.13693 | |
Mesh Learning Using Persistent Homology on the Laplacian Eigenfunctions. Yunhao Zhang, Haowen Liu, Paul Rosen, Mustafa Hajij. International Geometry Summit Poster, 2019. | |
Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing.
Lin Yan, Yaodong Zhao, Paul Rosen, Carlos Scheidegger, Bei Wang. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2018. arXiv:1806.08460 |
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Using Topological Data Analysis to Infer the Quality in Point Cloud-based 3D Printed Objects.
Paul Rosen, Mustafa Hajij, Junyi Tu, Tanvirul Arafin, Les Piegl. Computer Aided Design & Applications,16(3), pages 519-527, 2019. | |
DimReader: Axis Lines That Explain Non-Linear Projections.
Rebecca Faust, David Glickenstein, Carlos Scheidegger. IEEE Transactions on Visualization and Computer Graphics (InfoVis 2018), 25(1), pages 481-490, 2019. ArXiv:1710.00992 | |
Year 3 (2017 - 2018) | |
A Complete Characterization of the 1-DimensionalIntrinsic Čech Persistence Diagrams for Metric Graphs.
Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang and Lori Ziegelmeier. Association for Women in Mathematics Series: Research in Computational Topology Editors: Erin Chambers, Brittany Terese Fasy, Lori Ziegelmeier. 2018. arXiv:1512.04108. DOI: 10.1007/978-3-319-89593-2 | |
Visual Exploration of Semantic Relationships in Neural Word Embeddings.
Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics (InfoVis 2017), 24(1), pages 553-562, 2018. DOI: 10.1109/TVCG.2017.2745141 | |
A Vector Field Design Approach to Animated Transitions. Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu. IEEE Transactions on Visualization and Computer Graphics, 24(9), pages 2487-2500, 2018. DOI: 10.1109/TVCG.2017.2750689 | |
Discrete Stratified Morse Theory: A User's Guide.
Kevin Knudson and Bei Wang. International Symposium on Computational Geometry (SOCG), 2018. DOI:10.4230/LIPIcs.SoCG.2018.54 arXiv:1801.03183. | |
Sheaf-Theoretic Stratification Learning.
Adam Brown and Bei Wang. International Symposium on Computational Geometry (SOCG), 2018. DOI:10.4230/LIPIcs.SoCG.2018.14 arXiv:1712.07734. | |
Vietoris-Rips and Čech Complexes of Metric Gluings.
Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang and Lori Ziegelmeier. International Symposium on Computational Geometry (SOCG), 2018. DOI:10.4230/LIPIcs.SoCG.2018.3 arXiv:1712.06224. | |
Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology.
Mustafa Hajij, Bei Wang, Carlos Scheidegger, Paul Rosen. Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2018. DOI: 10.1109/PacificVis.2018.00024 arXiv:1707.06683 | |
The Shape of an Image: A Study of Mapper on Images.
Alejandro Robles, Mustafa Hajij, Paul Rosen. 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018. arXiv Version: arXiv:1710.09008. | |
Visualizing Sensor Network Coverage with Location Uncertainty.
Tim Sodergren, Jessica Hair, Jeff M. Phillips and Bei Wang. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2017. DOI: 10.1109/VDS.2017.8573448 ArXiv:1710.06925 | |
Open Problems in Computational Topology.
Brittany Terese Fasy and Bei Wang (with contributions by members of the WinCompTop community). SIGACT NEWS Open Problems Column, Edited by Bill Gasarch, 48(3), 2017. Online Version: SIGACT NEWS open problems column. | |
Topology, Computation and Data Analysis (Dagstuhl Seminar 17292) Editors: Hamish Carr, Michael Kerber, and Bei Wang. Report from Dagstuhl Seminar, 2018. Online Version: Topology, Computation and Data Analysis (Dagstuhl Seminar 17292). |
Year 2 (2016 - 2017) | |
Visualizing High-Dimensional Data: Advances in the Past Decade.
Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(3), pages 1249-1268, 2017. DOI: 10.1109/TVCG.2016.2640960 Survey Website (Maintained by Shusen Liu). | |
Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets.
Zhe Wang, Nivan Ferreira, Youhao Wei, Aarthy Sankari Bhaskar and Carlos Scheidegger. IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(1), pages 681-690, 2017. DOI: 10.1109/TVCG.2016.2598694 | |
DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates.
Hoa Nguyen and Paul Rosen. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017. DOI: 10.1109/TVCG.2017.2661309 | |
Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data.
Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Bei Wang, Brian Summa and Valerio Pascucci. Eurographics Conference on Visualization (EuroVis), 2016. Computer Graphics Forum (CGF), 35(3), pages 1-10, 2016. DOI: 10.1111/cgf.12876 | |
Exploring the Evolution of Pressure-Perturbations to
Understand Atmospheric Phenomena.
Wathsala Widanagamaachchi, Alexander Jacques, Bei Wang, Erik Crosman, Peer-Timo Bremer, Valerio Pascucci and John Horel. IEEE Pacific Visualization Symposium (PacificVis), 2017. DOI: 10.1109/PACIFICVIS.2017.8031584 | |
A Hybrid Solution to Calculating Augmented Join Trees of 2D Scalar Fields in Parallel.
Paul Rosen, Junyi Tu and Les Piegl. CAD Conference and Exhibition (Accepted, Extended Abstract), 2017. DOI: 10.14733/cadconfP.2017.32-36 | |
Visual Exploration of Multiway Dependencies in Multivariate Data.
Hoa Nguyen, Paul Rosen and Bei Wang. ACM SIGGRAPH ASIA Symposium on Visualization, pages 1-8, 2016. DOI: 10.1145/3002151.3002162 | |
Year 1 (2015 - 2016) | |
Kernel Partial Least Squares Regression for Relating Functional Brain Network Topology to Clinical Measures of Behavior.
Eleanor Wong, Sourabh Palande, Bei Wang, Brandon Zielinski, Jeffrey Anderson and P. Thomas Fletcher. International Symposium on Biomedical Imaging (ISBI), 2016. DOI: 10.1109/ISBI.2016.7493506 | |
Convergence between Categorical Representations of Reeb Space and Mapper.
Elizabeth Munch and Bei Wang*. International Symposium on Computational Geometry (SOCG), 2016. DOI: 10.4230/LIPIcs.SoCG.2016.53 arXiv:1512.04108. | |
Exploring Persistent Local Homology in Topological Data Analysis.
Brittany T. Fasy and Bei Wang*. Special session on Topological Methods in Data Science and Analysis, IEEE International Conference on Acoustics, Speech and Signal Process (ICASSP), 2016. DOI: 10.1109/ICASSP.2016.7472915 | |
Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.
Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia and Valerio Pascucci. IEEE Pacific Visualization (PacificVis), 2016. IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(6), pages 1683-1693, 2016. DOI: 10.1109/TVCG.2016.2534538 Supplemental Video. Vortex Video. Best Paper Award at PacificVis 2016! |
Manuscripts |
Uncertainty Visualization for Graph Coarsening via Local Adjusted Rand Indices and Co-Occurrences.
Fangfei Lan, Sourabh Palande, Michael Young, Bei Wang Manuscript, 2020. | |
Topological Simplifications of Hypergraphs.
Youjia Zhou, Archit Rathore, Emilie Purvine, Bei Wang Manuscript, 2020. | |
Gazing Into the Metaboverse: Automated Exploration and Contextualization of Metabolic Data.
Jordan A. Berg, Youjia Zhou, T. Cameron Waller, Yeyun Ouyang, Sara M. Nowinski, Tyler Van Ry, Ian George, James E. Cox, Bei Wang, Jared Rutter. Manuscript, 2020. bioRxiv:10.1101/2020.06.25.171850v1. | |
Local Versus Global Distances for Zigzag Persistence Modules.
Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Manuscript, 2019. arXiv:1903.08298. | |
Mapper on Graphs for Network Visualization.
Mustafa Hajij, Bei Wang, Paul Rosen. Manuscript, 2018. arXiv:1804.11242. |
Softwares |
Metaboverse: Automated Exploration and Contextualization of Metabolic Data.
https://github.com/Metaboverse/ Metaboverse is an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other global reaction networks. Reference: Gazing Into the Metaboverse: Automated Exploration and Contextualization of Metabolic Data, 2020. | |
MOG: Mapper on Graphs. https://github.com/USFDataVisualization/MapperOnGraphs MOG is designed for relationship preserving clustering of large graphs for graph visualization. Reference: Mapper on Graphs for Network Visualization, 2018. |
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TopoLines: Topological Smoothing for Line Charts. https://github.com/USFDataVisualization/TopoLines TopoLines is a method for smoothing line charts by leveraging techniques from topological data analysis. Publication: TopoLines: Topological Smoothing for Line Charts, 2020 |
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PHFDL: Persistent Homology Guided Force-Directed Graph Layouts.
https://github.com/USFDataVisualization/PersistentHomologyOnGraphs PHFDL implements persistent homology guided force-directed graph layouts. Publication: Persistent Homology Guided Force-Directed Graph Layouts, 2020. |
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AMT: Interactive Visualization of Labeled Merge Trees and Their 1-Center https://github.com/tdavislab/amt AMT computes structure averages of merge trees, with applications in neuron morphology. Publication: A Structural Average of Labeled Merge Trees for Uncertainty Visualization, 2020. |
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TopoAct: Visually Exploring the Shape of Activations in Deep Learning. https://github.com/tdavislab/TopoAct/ TopoAct is a visual exploration system to study topological summaries of activation vectors in neural networks. Publication: TopoAct: Visually Exploring the Shape of Activations in Deep Learning, 2020. |
Presentations, Educational Development and Broader Impacts |
Year 5 (2019 - 2020) |
Bei Wang Invited Talk (virtual): Topology as a knob for machine learning at MBI Optimal Transport Workshop: Optimal Transport, Topological Data Analysis and Applications to Shape and Machine Learning, July 27-31, 2020. |
Year 4 (2018 - 2019) |
Paul Rosen Invited Talk: Interacting with Data Using Geometry and Topology, at Early Career in Visualization Summer Camp, Nashville TN, June, 2019. |
Year 3 (2017 - 2018) |
Bei Wang Conference Talk: Discrete Stratified Morse Theory: A User's Guide, at 34th International Symposium on Computational Geometry (SOCG), June 11-14, 2018. |
Year 2 (2016 - 2017) |
Bei Wang Invited Talk: Relating Functional Brain Network Topology to Clinical Measures
of Behavior in Autism, at BIRS Workshop Topological Methods in Brain Network Analysis, May 7-12, 2017. |
Year 1 (2015 - 2016) |
Bei Wang Lecturer: at Hi-GEAR (Girls Engineering Abilities Realized) Camp,
part of Engineering Summer Camps at the University of Utah, June 13-17, 2016.
Hi-GEAR is designed to expose young women (currently in 9th-12th grade) to a variety of engineering and computer science careers with hands-on experiential learning and collaborative team projects. |
Students and Postdocs Dates are associated with the project |
Lin Yan (Graduate RA, 2017 - 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah linyan AT sci.utah.edu http://www.sci.utah.edu/people/linyan.html Nithin Chalapathi (REU, Undergraduate RA, 2019 - 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah nithin.ch10 AT gmail.com http://www.sci.utah.edu/people/nithin.html Youjia Zhou (Graduate RA, 2019 - 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah zhou325 AT sci.utah.edu https://www.sci.utah.edu/people/zhou325.html Fangfei Lan (Graduate RA, 2019 - 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah fangfei.lan AT sci.utah.edu https://www.sci.utah.edu/people/fangfei.lan.html Archit Rathore (Graduate RA, 2019 - 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah archit AT sci.utah.edu https://www.sci.utah.edu/people/archit.html Sourabh Palande (Graduate RA, 2015 - 2020, graduated Fall 2020) School of Computing and Scientific Computing and Imaging Institute University of Utah sourabh AT sci.utah.edu PostDoc @ Michigan State University Ghulam Quadri (Graduate RA, PhD, 2018 - 2020) Computer Science & Engineering Department University of South Florida ghulamjilani AT mail.usf.edu http://jiquadcs.com/ Tanmay Kotha (Graduate RA, MS, 2018 - 2020) Computer Science & Engineering Department University of South Florida tanmay AT mail.usf.edu Chukwubuikem Ume-Ugwa (REU, Undergraduate RA, BS, 2018 - 2019) Computer Engineering and Chemical Engineering University of South Florida cumeugwa AT mail.usf.edu Junyi Tu (Graduate RA, 2016 - 2018) Department of Mathematics and Statistics University of South Florida junyi AT mail.usf.edu Mustafa Hajij (Postdoc, 2016 - 2018) Department of Mathematics & Statistics University of South Florida mhajij AT usf.edu Assistant Professor @ Santa Clara University Adam Brown (Graduate RA, 2017 - 2019, Graduated Spring 2019) Department of Mathematics University of Utah abrown AT math.utah.edu PostDoc @ IST Austria Yaodong Zhao (Graduate RA, 2017 - 2019, Graduated Spring 2019) School of Computing and Scientific Computing and Imaging Institute University of Utah yaodong.zhao AT utah.edu First job @ LeanTaaS Yiliang Shi (Undergraduate RA, 2017 - 2018, Graduated Spring 2018) School of Computing and Scientific Computing and Imaging Institute University of Utah Honorable Mention in the Computing Research Association (CRA) Outstanding Undergraduate Researchers, 2018. Graduate school @ Columbia University, PhD. William Garnes (REU, Undergraduate RA, 2016 - 2017, Graduated Spring 2018) School of Computing and Scientific Computing and Imaging Institute University of Utah wagarnes AT sci.utah.edu College of Engineering Scholarship, 2017 - 2018 GEM fellowship award, 2018 Graduate school @Clemson University, MS. Ashley Suh (Undergraduate RA, 2017 - 2018, Graduated Spring 2018) Computer Science and Engineering University of South Florida asuh AT mail.usf.edu Graduate School @ Tufts University, PhD. Tim Sodergren (Graduate RA, 2016 - 2017) School of Computing and Scientific Computing and Imaging Institute University of Utah tsodergren AT sci.utah.edu Matthew Howa (REU, Undergraduate RA, 2016 - 2017) School of Computing and Scientific Computing and Imaging Institute University of Utah mahowa AT sci.utah.edu Todd Harry Reeb (Graduate RA, Summer 2016) Department of Mathematics University of Utah reeb AT math.utah.edu Jackson Pawson (Undergraduate RA, 2015 - 2016, Graduated Spring 2016) Computer Science and Engineering University of South Florida jacksonpawson AT gmail.com |
Collaborators |
Tom Fletcher |
Acknowledgement |
This material is based upon work supported or partially supported by the National Science Foundation under Grant No.1513616 and 1513651, project titled "III: Medium: Collaborative Research:
Topological Data Analysis for Large Network Visualization." |