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Quynh Quang Ngo

Dr.

Research Associate
Visualization Research Center (VISUS)
Sedlmair Group

Contact

+49 711 685 88623
Email

Allmandring 19
70569 Stuttgart
Germany
Room: 01.031

Publications

2023
1. Bauer, Ruben ; Ngo, Quynh Quang ; Reina, Guido ; Frey, Steffen ; Flemisch, Bernd ; Hauser, Helwig ; Ertl, Thomas ; Sedlmair, Michael: Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment. In: Transport in Porous Media, Transport in Porous Media. (2023)
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  BibTeX
  @article{bauer2023visual, abstract = {We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO2 into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relationship between different simulations. We test different metrics and find that using the machine-learning model “S4” (tailored to the present study) as metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. All views support linking and brushing interaction to allow users to select and highlight subsets of the data simultaneously across multiple views. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.}, archiveprefix = {arXiv}, author = {Bauer, Ruben and Ngo, Quynh Quang and Reina, Guido and Frey, Steffen and Flemisch, Bernd and Hauser, Helwig and Ertl, Thomas and Sedlmair, Michael}, doi = {10.1007/s11242-023-02019-y}, eprint = {2302.05447}, journal = {Transport in Porous Media}, primaryclass = {cs.HC}, title = {Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment}, url = {/brokenurl# https://link.springer.com/article/10.1007/s11242-023-02019-y#citeas}, year = 2023 }
  Link
  /brokenurl# https://link.springer.com/article/10.1007/s11242-023-02019-y#citeas
2. Chen, Kun-Ting ; Ngo, Quynh Quang ; Kurzhals, Kuno ; Marriott, Kim ; Dwyer, Tim ; Sedlmair, Michael ; Weiskopf, Daniel: Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology. In: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, Proceedings of the 2023 Symposium on Eye Tracking Research and Applications. Tubingen, Germany : Association for Computing Machinery, 2023 — ISBN 9798400701504
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  @inproceedings{10.1145/3588015.3589841, abstract = {We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.}, address = {New York, NY, USA}, articleno = {67}, author = {Chen, Kun-Ting and Ngo, Quynh Quang and Kurzhals, Kuno and Marriott, Kim and Dwyer, Tim and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {Proceedings of the 2023 Symposium on Eye Tracking Research and Applications}, doi = {10.1145/3588015.3589841}, isbn = {9798400701504}, location = {Tubingen, Germany}, numpages = {7}, publisher = {Association for Computing Machinery}, series = {ETRA '23}, title = {Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology}, url = {https://doi.org/10.1145/3588015.3589841}, year = 2023 }
  Link
  https://doi.org/10.1145/3588015.3589841
3. Heyen, Frank ; Ngo, Quynh Quang ; Sedlmair, Michael: Visual Overviews for Sheet Music Structure. In: Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, 2023, S. 692--699
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  BibTeX
  @inproceedings{heyen2023visual, abstract = {We propose different methods for alternative representation and visual augmentation of sheet music that help users gain an overview of general structure, repeating patterns, and the similarity of segments. To this end, we explored mapping the overall similarity between sections or bars to colors. For these mappings, we use dimensionality reduction or clustering to assign similar segments to similar colors and vice versa. To provide a better overview, we further designed simplified music notation representations, including hierarchical and compressed encodings. These overviews allow users to display whole pieces more compactly on a single screen without clutter and to find and navigate to distant segments more quickly. Our preliminary evaluation with guitarists and tablature shows that our design supports users in tasks such as analyzing structure, finding repetitions, and determining the similarity of specific segments to others.}, archiveprefix = {arXiv}, author = {Heyen, Frank and Ngo, Quynh Quang and Sedlmair, Michael}, booktitle = {Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023}, doi = {doi.org/10.48550/arXiv.2308.06140}, eprint = {2308.06140}, month = {11}, pages = { 692--699}, primaryclass = {cs.HC}, title = {Visual Overviews for Sheet Music Structure}, url = {https://arxiv.org/abs/2308.06140}, year = 2023 }
  Link
  https://arxiv.org/abs/2308.06140
2022
1. Ngo, Quynh Quang ; Dennig, Frederik L. ; Keim, Daniel A. ; Sedlmair, Michael: Machine Learning Meets Visualization – Experiences and Lessons Learned. In: it - Information Technology, it - Information Technology. Bd. 64 (2022), Nr. 4–5, S. 169–180
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  @article{NgoDennigKeimSedlmair+2022+169+180, abstract = {In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.}, author = {Ngo, Quynh Quang and Dennig, Frederik L. and Keim, Daniel A. and Sedlmair, Michael}, doi = {doi:10.1515/itit-2022-0034}, journal = {it - Information Technology}, lastchecked = {2022-09-20}, number = {4-5}, pages = {169–180}, title = {Machine Learning Meets Visualization – Experiences and Lessons Learned}, url = {https://doi.org/10.1515/itit-2022-0034}, volume = 64, year = 2022 }
  Link
  https://doi.org/10.1515/itit-2022-0034
2. Heyen, Frank ; Ngo, Quynh Quang ; Kurzhals, Kuno ; Sedlmair, Michael: Data-Driven Visual Reflection on Music Instrument Practice. In: ACM CHI Workshop on Intelligent Music Interfaces (IMI), ACM CHI Workshop on Intelligent Music Interfaces (IMI) : ACM, 2022
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  @inproceedings{heyen2022datadriven, abstract = {We propose a data-driven approach to music instrument practice that allows studying patterns and long-term trends through visualization. Inspired by life logging and fitness tracking, we imagine musicians to record their practice sessions over the span of months or years. The resulting data in the form of MIDI or audio recordings can then be analyzed sporadically to track progress and guide decisions. Toward this vision, we started exploring various visualization designs together with a group of nine guitarists, who provided us with data and feedback over the course of three months.}, author = {Heyen, Frank and Ngo, Quynh Quang and Kurzhals, Kuno and Sedlmair, Michael}, booktitle = {ACM CHI Workshop on Intelligent Music Interfaces (IMI)}, doi = {10.48550/arxiv.2203.13320}, publisher = {ACM}, title = {Data-Driven Visual Reflection on Music Instrument Practice}, url = {https://doi.org/10.48550/arxiv.2203.13320}, year = 2022 }
  Link
  https://doi.org/10.48550/arxiv.2203.13320

Quynh Quang Ngo

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2023

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Quynh Quang Ngo

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2023

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2022

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