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Machine Learning and Uncertainty

Working Group 8.44

Profile

The Machine Learning and Uncertainty group 8.44 develops and validates machine learning techniques with applications in neuroscience and medicine. Current research interests include  

  • Solutions to the brain's electromagnetic inverse problem using Bayesian methods 
  • Brain functional connectivity analysis using electro- and magnetoencephalography 
  • Interpretation/explanation of machine learning models  
  • Creation of synthetic reference datasets and benchmarking procedures to assess quality aspects of computational methods  
  • Uncertainty estimates for inverse problems 

Current applications include 

  • Diagnosis and characterization of psychiatric and neurological disorders 
  • Intensive care unit (ICU) data 
  • Brain-computer interfaces 

The group is tightly linked with the Brain and Data Science Group at Charité - Universitätsmedizin Berlin and the Department of Uncertainty, Inverse Modeling and Machine Learning at Technische Universität Berlin, both also led by Dr. Stefan Haufe. 

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Research/Development

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Services

The group currently does not offer any specific service. However, we are open to discussing machine learning and data science problems arising in other PTB groups. 

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