Visualization & Imaging
Visualisation and imaging are two sides of the same coin, both lying on the border between mathematics and computer science. Visualization is occupied with making structures, objects, data and processes visible. Imaging works in the other direction, seeking to reconstruct information on the structures, objects and processes behind existing pictures or films.
Our research groups at the TUM are particularly active in the following areas of visualization and imaging:
- theoretical fundaments (what are suitable mathematical methods for the visualisation scenarios posed),
- the creation of software systems for interacitve visualization (e.g. Cinderella/CindyJS),
- the connections between discrete and continual processes (in close cooperation with the SFB/Transregio Discretization in Geometry and Dynamics),
- the reconstruction of incomplete images (Image Inpainting and reconstruction).
Professors
Related research groups
Research News
© Eraslan, Simon, Theis et al.: Single cell RNA-seq denoising using a deep count autoencoder. Nature Communications.
AI against errors
Using single-cell analysis, researchers study the behavior of cells. But the method is prone to errors. A deep learning algorithm can now detect these errors.
© Aaron Montag
Be my Valentine! Say it with Mathematics
Send Valentine greetings with a customizable algebraic equation. With the widget "Suess" by Aaron Montag and IMAGINARY, you can see what (x²+((1+b)y)²+z²-1)³-x²z³-ay²z³=0 can look like.
© Josch13/pixabay.com
Statistical model of the Mekong water level
When the Mekong overflows its banks, millions of people are threatened by flooding. With a statistical model by Prof. Klüppelberg and a research team, the water level can be predicted.
The Discrete Mathematics of Democracy
How fair are democratic elections? And what does fair mean? In his lecture on the agony with choice, Prof. Peter Gritzmann reveals how mathematics can bring more justice into democracy.
© MuDS
Graduate School for Data Science
Die digitalisierte Forschung liefert riesige Datenmengen - mit großem Potenzial. Deshalb will die Munich School for Data Science jetzt Expert*innen ausbilden.
First place for Zi Ye
The TUM postgraduate Zi Ye has been awarded first place in the Best Paper Award of the Symposium on Geometry Processing 2018. He convinced with his paper about intrinsic and extrinsic Dirac operators for geometry processing.
Proof of quantum computer advantage
Quantum computers can solve problems more efficiently than conventional computers - at least that’s the vision. Robert König and a team of researchers prove that they do indeed offer advantages.
Felix Happach wins Paper Award
"Good Clusterings Have Large Volume". With this paper Felix Happach wins the second prize in the Student Paper Prize of the INFORMS Optimization Society.