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Machine Learning and Uncertainty

Working Group 8.44


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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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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