Fakultät Informatik

Dr. Mihaela Jarema

Topics of Research

Uncertainty Visualization, Ensemble Visualization.

How to get in Touch

eMail:

 mihaela.jarema(at)tum.de

Phone:

+49 89 289 19451

Fax:

+49 89 289 19462

Room:

02.13.060

Address:

Technische Universität München
Informatik 15 (Computer Graphik & Visualisierung)
Boltzmannstrasse 3
85748 Garching bei München
Germany

Technical Reports

Probability Distributions for Gradient Orientations in Uncertain 3D Scalar Fields
T. Pfaffelmoser, M. Mihai, R. Westermann, Technical Report, TU München, 2012 [Download][Bibtex]

Recent Publications

 Visualizing the Central Tendency of Ensembles of Shapes
I. Demir, M. Jarema, R. Westermann, Proceedings of ACM SIGGRAPH Asia 2016 Symposium on Visualization, 2016 (accepted for publication)

 Comparative Visual Analysis of Transport Variability in Flow Ensembles
M. Jarema, J. Kehrer, R. Westermann
 J. WSCG, 24(1):25-34, 2016.

 Comparative Visual Analysis of Vector Field Ensembles
M. Jarema, I. Demir, J. Kehrer, R. Westermann.
In Proc. IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), 2015.

 Visualizing the Stability of Critical Points in Uncertain Scalar Fields
M. Mihai and R. Westermann, Computers & Graphics 41C (2014), pp. 13-25 [Download] [Bibtex]

Talks

Comparative Visual Analysis of Transport Variability in Flow Ensembles, WSCG 2016, June 2016, Pilsen, Czech Republic.

Comparative Visual Analysis of Vector Field Ensembles, IEEE VAST 2015, October 2015, Chicago, Illinois, USA.

Visualizing the Stability of Critical Points in Uncertain Scalar Fields (Invited Talk), SCCG 2015, April 2015, Smolenice castle, Slovakia.

Glyph-based Analysis of Multimodal Directional Distributions in Vector Field Ensembles, April 2015, EGU 2015-12240, Vienna, Austria.

Visualizing the Variability of Gradients in Uncertain 2D Scalar Fields (TVCG journal paper), IEEE VIS 2013, October 2013, Atlanta, Georgia, USA.

 

 

 

News

Bachelor and Master thesis in the following areas:
- A remote rendering system for point cloud data (in collaboration with industry)

- Deep learning for improved weather forecasting

- Learning trajectory clustering using neural network
s
- Learning Level-of-Detail representations for point clouds


- In collaboration with partners from industry, we have a number of thesis topics available in the area of point-based rendering, geo-localization using public data, scene fusion from different viewpoints. If you are interested, please contact  westermann(at)tum.de

 

- One PhD position on   Turbulence Visualization is available at the Computer Graphics & Visualization group.