Knowledge Discovery and Synthesis
The "Knowledge Discovery and Synthesis" group is investigating methods for identifying potentially complex patterns in data, and methods for synthesizing information from several sources. The spectrum of our work ranges from meta-analysis techniques for clinical trials to machine learning techniques, in particular artificial intelligence/deep learning, for integrating molecular and clinical data.
Head
- Machine Learning (esp. Deep Learning)
- Integration of Molecular and Clinical Data
AG Machine Learning
- Deep Generative Models
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Analysis for Cellular and Molecular Data
- Machine Learning (esp. Deep Learning)
- Distributed Data
- Deep Generative Models
- Machine Learning (esp. Deep Learning)
- Neural Differential Equations
- Generative Models
- Machine Learning (esp. Deep Learning)
- Feature Learning in High-Dimensional Molecular-Diagnostic Data
- Machine Learning (esp. Deep Learning)
- Resilience and Vulnerabilty
- Age-Period-Cohort Analysis
- Deep Generative Models
- Design of Single Cell RNA-Seq Experiments
AG Meta-Analyse
- Meta-analysis
- Network Meta-analysis
- Meta-analysis
- Network Meta-analysis
- Meta-analysis
- Network Meta-analysis
- Software Development
- Meta-Analysis
- Network Meta-Analysis
- Meta-Analysis of Diagnostic Accuracy Studies
- Meta-Analysis
- Software Development
AG STRATOS
- STRATOS
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Multivariable Model-Building in Regression Models
-
Model Stability and Shrinkage
-
Simulation Studies
-
Variable Selection
-
Simulation Studies
-
High-Dimensional Data
Alumni
Dr. Sara Balduzzi
- Meta-Analysis
- Meta-Analysis of Diagnostic Accuracy Studies
Federico Bonofiglio
Caroline Broichhagen
Stefan Lenz
- Algorithms, (esp. in the Field of Deep Learning)
- Software Development and API Design