M.Sc. Kevin Höhlein
Technical University of Munich
Informatics 15 - Chair of Computer Graphics and Visualization (Prof. Westermann)
Postal address
Postal:
Boltzmannstr. 3
85748 Garching b. München
- Phone: +49 (89) 289 - 19454
- kevin.hoehlein@tum.de
Research
- Deep learning for weather prediction and meteorological data analysis
- Explainability of machine learning predictions
- Probabilistic modelling and generative deep learning
- Nonlinear dynamics and chaos theory
Topics for Bachelor and Master Theses
Parallel Coordinates Plots (PCPs, e.g., Stumpfegger et al., 2022) are a valuable tool for visualizing high-dimensional data. In contrast to well-known scatter plots and similar bivariate data visualizations, PCPs allow for displaying many parameters at the same time along parallel coordinate axes, which enables the user to identify multi-parameter relations in the data, which would remain hidden in other visualizations. PCPs are most powerful when used in an interactive setting, where the data analyst can interactively exchange the axis ordering or select subsets of the data for more detailed analysis. Especially the subselection process, also known as brushing, can improve the visibility of hidden structure in the data. Finding good brushes, however, can be a tedious task when dealing with very high-dimensional data, since a lot of brushing configurations have to be examined and compared manually. A partially automated brushing process, in which the user receives proposals of potentially interesting brushes could therefore increase the usability of PCPs in data-intensive analysis scenarios. The task in this thesis will be to explore one way of achieving such an automated brushing, based on neural-network based similarity learning. Building up on the idea of generative adversarial learning, a differentiable brushing approach is to be implemented and compared against competitor approaches for smart brushing from the relevant literature.
Publications
Postprocessing of Ensemble Weather Forecasts Using Permutation-Invariant Neural Networks
Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann, Sebastian Lerch
Artif. Intell. Earth Syst., 3, e230070 (2024).
Neural fields for interactive visualization of statistical dependencies in 3D simulation ensembles
Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
In M. Guthe, & T. Grosch (Eds.), Vision, Modeling, and Visualization. The Eurographics Association (2023).
Deep Learning–Based Parameter Transfer in Meteorological Data
Fatemeh Farokhmanesh, Kevin Höhlein, Rüdiger Westermann
Artif. Intell. Earth Syst., 2, e220024 (2023).
GPU accelerated scalable parallel coordinates plots
Josef Stumpfegger, Kevin Höhlein, Rüdiger Westermann
Computers & Graphics (2022).
Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
Kevin Höhlein, Sebastian Weiss, Rüdiger Westermann
In J. Bender, M. Botsch, & D. A. Keim (Eds.), Vision, Modeling, and Visualization. The Eurographics Association (2022).
Learning Multiple-Scattering Solutions for Sphere-Tracing of Volumetric Subsurface Effects
Ludwig Leonard, Kevin Höhlein, Rüdiger Westermann
Computer Graphics Forum, 40(2), pp. 165-178 (2021).
A Comparative Study of Convolutional Neural Network Models for Wind Field Downscaling
Kevin Höhlein, Michael Kern, Timothy Hewson, Rüdiger Westermann
arXiv:2008.12257, Meteorological Applications, 27, e1961 (2020).
Lyapunov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators
Kevin Höhlein, Felix P. Kemeth, and Katharina Krischer
Phys. Rev. E, 100, 022217 (2019).
An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich, Tom Bertalan, Kevin Höhlein, Qianxiao Li, Erik M. Bollt, Ronen Talmon, Katharina Krischer, and Ioannis G. Kevrekidis
IEEE Access, Vol. 6, pp. 77402-77413 (2018).