COMFORT

COMFORT aims to achieve breakthroughs in developing compact, flexible, and robust machine learning models for image, audio, and network data. In doing so, its application-oriented research program will advance the mathematical understanding of machine learning at the intersection of effectiveness and robustness.

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Other projects


GLAM, third-party funded project, Helmholtz Imaging
 

GLAM: Generative lung architecture modeling

This project is developing generative methods for designing bio-printable lung tissues across a spectrum of disease severity in the specific context of mouse and human lung disease.
Decorative image
 

Foundations of Supervised Deep Learning for Inverse Problems

Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.
Decorative image
 

QGRIS: Quantitative Gamma-Ray Imaging System

Compton cameras are used for the radiological characterization of nuclear power plants. In this project, a suitable camera system is designed, and the associated algorithms for image reconstruction and nuclide characterization are implemented as user software.