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David Hägele

M. Sc.

Contact

+49 711 685 88625
Email

Allmandring 19
70569 Stuttgart
Deutschland
Room: 00.010

Publications

2023
1. Hägele, David ; Krake, Tim ; Weiskopf, Daniel: Uncertainty-Aware Multidimensional Scaling. In: IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics. Bd. 29 (2023), Nr. 1, S. 23--32
  BibTeX
  @article{haegele2023uamds, abstract = {We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space to formulate a generalized stress. In this way, our generic model supports arbitrary distributions and various stress types. We use our uncertainty-aware multidimensional scaling (UAMDS) concept to derive a formulation for the case of normally distributed random vectors and a squared stress. The resulting minimization problem is numerically solved via gradient descent. We complement UAMDS by additional visualization techniques that address the sensitivity and trustworthiness of dimensionality reduction under uncertainty. With several examples, we demonstrate the usefulness of our approach and the importance of uncertainty-aware techniques.}, author = {Hägele, David and Krake, Tim and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3209420}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {23--32}, title = {Uncertainty-Aware Multidimensional Scaling}, url = {https://doi.org/10.1109/TVCG.2022.3209420}, volume = 29, year = 2023 }
  Link
  https://doi.org/10.1109/TVCG.2022.3209420
  DOI
  10.1109/TVCG.2022.3209420
2022
1. Hägele, David ; Schulz, Christoph ; Beschle, Cedric ; Booth, Hannah ; Butt, Miriam ; Barth, Andrea ; Deussen, Oliver ; Weiskopf, Daniel: Uncertainty Visualization: Fundamentals and Recent Developments. In: it - Information Technology, it - Information Technology. Bd. 64 (2022), Nr. 4–5, S. 121–132
  BibTeX
  @article{haegeleIt2022, abstract = {This paper provides a brief overview of uncertainty visualization along with some fundamental considerations on uncertainty propagation and modeling. Starting from the visualization pipeline, we discuss how the different stages along this pipeline can be affected by uncertainty and how they can deal with this and propagate uncertainty information to subsequent processing steps. We illustrate recent advances in the field with a number of examples from a wide range of applications: uncertainty visualization of hierarchical data, multivariate time series, stochastic partial differential equations, and data from linguistic annotation.}, author = {H\"agele, David and Schulz, Christoph and Beschle, Cedric and Booth, Hannah and Butt, Miriam and Barth, Andrea and Deussen, Oliver and Weiskopf, Daniel}, doi = {10.1515/itit-2022-0033}, journal = {it - Information Technology}, number = {4–5}, pages = {121–132}, title = {Uncertainty Visualization: Fundamentals and Recent Developments}, url = {https://doi.org/10.1515/itit-2022-0033}, volume = 64, year = 2022 }
  Link
  https://doi.org/10.1515/itit-2022-0033
  DOI
  10.1515/itit-2022-0033
2. Hägele, David ; Krake, Tim ; Weiskopf, Daniel: Uncertainty-Aware Multidimensional Scaling. In: IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics. Bd. 29 (2022), Nr. 1, S. 1–10
  BibTeX
  @article{haegele2023uamds, abstract = {We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space to formulate a generalized stress. In this way, our generic model supports arbitrary distributions and various stress types. We use our uncertainty-aware multidimensional scaling (UAMDS) concept to derive a formulation for the case of normally distributed random vectors and a squared stress. The resulting minimization problem is numerically solved via gradient descent. We complement UAMDS by additional visualization techniques that address the sensitivity and trustworthiness of dimensionality reduction under uncertainty. With several examples, we demonstrate the usefulness of our approach and the importance of uncertainty-aware techniques.}, author = {Hägele, David and Krake, Tim and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3209420}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {1-10}, title = {Uncertainty-Aware Multidimensional Scaling}, url = {https://doi.org/10.1109/TVCG.2022.3209420}, volume = 29, year = 2022 }
  Link
  https://doi.org/10.1109/TVCG.2022.3209420
  DOI
  10.1109/TVCG.2022.3209420
2021
1. Hägele, David ; Abdelaal, Moataz ; Oguz, Ozgur S. ; Toussaint, Marc ; Weiskopf, Daniel: Visual analytics for nonlinear programming in robot motion planning. In: Journal of Visualization, Journal of Visualization. (2021)
  BibTeX
  @article{hagele2021visual, abstract = {Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming.}, author = {Hägele, David and Abdelaal, Moataz and Oguz, Ozgur S. and Toussaint, Marc and Weiskopf, Daniel}, doi = {10.1007/s12650-021-00786-8}, issn = {18758975}, journal = {Journal of Visualization}, refid = {Hägele2021}, title = {Visual analytics for nonlinear programming in robot motion planning}, url = {https://doi.org/10.1007/s12650-021-00786-8}, year = 2021 }
  Link
  https://doi.org/10.1007/s12650-021-00786-8
  DOI
  10.1007/s12650-021-00786-8
2020
1. Hägele, David ; Abdelaal, Moataz ; Oguz, Ozgur S. ; Toussaint, Marc ; Weiskopf, Daniel: Visualization of Nonlinear Programming for Robot Motion Planning. In: Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, Proceedings of the 13th International Symposium on Visual Information Communication and Interaction. Eindhoven, Netherlands : Association for Computing Machinery, 2020 — ISBN 9781450387507
  BibTeX
  @inproceedings{haegele2020nonlinear, abstract = {Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving. We address the particularly hard challenge of supporting domain experts in handling, understanding, and trouble-shooting high-dimensional optimization with a large number of constraints. Leveraging visual analytics, users are supported in exploring the computation process of nonlinear constraint optimization. Our system was designed for robot motion planning problems and developed in tight collaboration with domain experts in nonlinear programming and robotics. We report on the experiences from this design study, illustrate the usefulness for relevant example cases, and discuss the extension to visual analytics for nonlinear programming in general.}, address = {New York, NY, USA}, articleno = {10}, author = {Hägele, David and Abdelaal, Moataz and Oguz, Ozgur S. and Toussaint, Marc and Weiskopf, Daniel}, booktitle = {Proceedings of the 13th International Symposium on Visual Information Communication and Interaction}, doi = {10.1145/3430036.3430050}, isbn = {9781450387507}, location = {Eindhoven, Netherlands}, numpages = {8}, publisher = {Association for Computing Machinery}, series = {VINCI '20}, title = {Visualization of Nonlinear Programming for Robot Motion Planning}, url = {https://doi.org/10.1145/3430036.3430050}, year = 2020 }
  Link
  https://doi.org/10.1145/3430036.3430050
  DOI
  10.1145/3430036.3430050

Teaching

A Curated Collection of Multivariate Timeseries for Visualization Benchmarks (Bachelor Forschungsprojekt)
Programmierung für Medieninformatik (Practical Course)
Datenstrukturen und Algorithmen

David Hägele

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2023

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2022

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2021

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David Hägele

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2023

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2022

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2021

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2020

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