David Hägele

According to our database, David Hägele authored at least 17 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models.

Daniel Klötzl, Ozan Tastekin, David Hägele, Marina Evers, Daniel Weiskopf

CoRR, August, 2025

Two Empirical Studies on Audiovisual Semiotics of Uncertainty.

Sita Vriend, David Hägele, Daniel Weiskopf

CoRR, May, 2025

Supplemental materials for "Two Empirical Studies on Audiovisual Semiotics of Uncertainty".

Sita Vriend, David Hägele, Daniel Weiskopf

Dataset, May, 2025

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess.

Tim Krake, Daniel Klötzl, David Hägele, Daniel Weiskopf

IEEE Trans. Vis. Comput. Graph., February, 2025

2024

Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions.

Luca Reichmann, David Hägele, Daniel Weiskopf

Dataset, September, 2024

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess - Supplemental Material.

Tim Krake, Daniel Klötzl, David Hägele, Daniel Weiskopf

Dataset, March, 2024

Maximum Entropy and Quantized Metric Models for Absolute Category Ratings.

Dietmar Saupe, Krzysztof Rusek, David Hägele, Daniel Weiskopf, Lucjan Janowski

IEEE Signal Process. Lett., 2024

Progressive Glimmer: Expanding Dimensionality in Multidimensional Scaling.

Marina Evers, David Hägele, Sören Döring, Daniel Weiskopf

CoRR, 2024

Visual Compositional Data Analytics for Spatial Transcriptomics.

David Hägele, Yuxuan Tang, Daniel Weiskopf

CoRR, 2024

UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox.

Patrick Paetzold, David Hägele, Marina Evers, Daniel Weiskopf, Oliver Deussen

Proceedings of the IEEE Workshop on Uncertainty Visualization: Applications, 2024

Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions.

Luca Reichmann, David Hägele, Daniel Weiskopf

Proceedings of the 14th IEEE Symposium on Large Data Analysis and Visualization, 2024

2023

Uncertainty-Aware Multidimensional Scaling.

David Hägele, Tim Krake, Daniel Weiskopf

IEEE Trans. Vis. Comput. Graph., 2023

2022

Supplemental Material for Uncertainty-Aware Multidimensional Scaling.

David Hägele, Tim Krake, Daniel Weiskopf

Dataset, October, 2022

Visual analytics for nonlinear programming in robot motion planning.

David Hägele, Moataz Abdelaal, Ozgur S. Oguz, Marc Toussaint, Daniel Weiskopf

J. Vis., 2022

Uncertainty visualization: Fundamentals and recent developments.

David Hägele, Christoph Schulz, Cedric Beschle, Hannah Booth, Miriam Butt, Andrea Barth, Oliver Deussen, Daniel Weiskopf

it Inf. Technol., 2022

2020

Supplemental Material for Visualization of Nonlinear Programming for Robot Motion Planning.

David Hägele, Moataz Abdelaal, Ozgur S. Oguz, Marc Toussaint, Daniel Weiskopf

Dataset, December, 2020

Visualization of nonlinear programming for robot motion planning.

David Hägele, Moataz Abdelaal, Ozgur S. Oguz, Marc Toussaint, Daniel Weiskopf

Proceedings of the VINCI 2020: The 13th International Symposium on Visual Information Communication and Interaction, 2020