DFG KI-FOR 5363

Research Programme

This research unit brings together experts from machine learning and statistics with a track record in biomedical applications to develop methods that integrate Deep Learning and statistics. The aim is to improve interpretability, uncertainty quantification and statistical inference for Deep Learning, and to improve modeling flexibility of statistical methods for structured data. In particular, it will develop methods that provide statistical inference for structured data by quantification of uncertainty, testing of hypotheses and adjustment for confounders, and that improve explanations of structured data through hybrid statistical and deep learning models, population- and distribution -level explanations, and robust sparse explanations.

All methods are created in a feedback loop with biomedical applications, to account for their data analysis needs in methods development, and to directly generate novel bio-medical insights using our newly developed methods. Applications include analysis of MRI, fMRI and microscopy images, longitudinal disease progression modeling, DNA sequence analysis, and genetic association studies. Furthermore, methods will be relevant far beyond these important use cases.

The KI-FOR 5363 has the following equal opportunity measures in place:

• Home office opportunity
• Facilitation opportunities in the form of student assistance during and after care-related leaves of absence
• Professional childcare outside of daycare opening hours (especially during research unit conferences and retreats)
• Mentoring program for female scientists
• Start-up project for a female postdoctoral fellow
• lectures and trainings on gender equality and anti-discrimination at the retreats of the research unit.