Uncertainty, Inverse Modeling and Machine Learning

Prof. Dr. Stefan Haufe




Prof. Dr.

Stefan Haufe

Head of Group


+49 (0) 30 314 70140

Einrichtung FG UNIML
Sekretariat MAR 4-4
Gebäude MAR
Raum 4.010
Adresse Marchstraße 23
10587 Berlin

Stefan Haufe is a professor of computer science (W2) and head of the UNIML group at Technische Universität Berlin. This is a joint appointment with the Physikalisch-Technische Bundesanstalt Berlin (PTB), where Dr. Haufe leads also leads Working Group 8.44 “Machine Learning and Uncertainty”. In addition, Dr. Haufe heads the European Research Council (ERC) funded Braindata Group at Charité - Universitätsmedizin Berlin.

His research is centred around the development and validation of signal processing, inverse modelling and ML techniques for neuroimaging and other medical data.

Curriculum Vitae

Since 05/2021Professor (W2), Head of FG UNIMLTU Berlin
Since 05/2021Head of Working Group 8.44PTB
Since 01/2019Head of ERC Research GroupCharité - Universitätsmedizin Berlin
07/2021-08/2021Parental leave 
04/2018-12/2018Parental leave 
01/2017-03/2018Postdoctoral Research AssociateTU Berlin
07/2016-08/2016Visiting ScholarUC San Francisco
07/2014-12/2016Marie Curie Postdoctoral FellowColumbia University and TU Berlin
11/2013-06/2014Postdoctoral Research AssociateCity College New York
03/2013-04/2013Visiting ScholarUC San Diego
09/2012-10/2012Visiting ScholarKorea University, Seoul
12/2011-10/2013Postdoctoral Research AssociateTU Berlin
09/2009-10/2009DAAD Visiting PhD StudentUniversity of Tokyo
02/2006-11/2011PhD StudentTU Berlin, with Bernstein Focus Neurotech. and Fraunhofer FIRST
07/2003-08/2003IAESTE Student TraineeYildiz Technical University, Istanbul
03/2003-05/2003Student TraineeSiemens TS, Braunschweig
10/2000-11/2005MSc Student in Computer ScienceMLU Halle-Wittenberg

Research Projects

05/2021-04/2026Heidenhain Junior Research GroupMachine Learning and UncertaintyPTB
05/2021-03/2024Einstein International Postdoc Fellowship (Fellow: Mahta Mousavi)FCInterpretationTUB
07/2020-06/2024DFG CRCReTune, project B05Charité
01/2019-06/2024ERC starting grantTrueBrainConnectTUB and Charité
07/2014-12/2016EU Marie Curie Postdoc FellowshipHyperscanning 2.0Columbia University and TUB



Langer, N.; Plomecka, M. B.; Tröndle, M.; Negi, A.; Popov, T.; Milham, M.; Haufe, S.
A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample
NeuroImage, 119348
Stephani, T.; Waterstraat, G.; Haufe, S.; Curio, G.; Villringer, A.; Nikulin, V. V.
Erratum: Stephani et al.,“Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses”
The Journal of Neuroscience, 42 (22) :4605–4605
Palmisano, C.; Beccaria, L.; Haufe, S.; Volkmann, J.; Pezzoli, G.; Isaias, I. U.
Gait initiation impairment in patients with Parkinson's disease and freezing of gait
Merk, T.; Peterson, V.; Köhler, R.; Haufe, S.; Richardson, R. M.; Neumann, W. J.
Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Experimental Neurology, 113993
Pellegrini, F.; Nikulin, V.; Haufe, S.
P 80 How to avoid measurement of spurious inter-regional functional connectivity from EEG–a simulation study
Clinical Neurophysiology 137, e60- :61
Schulz, M. A.; Bzdok, D.; Haufe, S.; Haynes, J. D.; Ritter, K.
Performance reserves in brain-imaging-based phenotype prediction
Kumral, D.; Cesnaite, E.; Beyer, F.; Hofmann, S. M.; Hensch, T.; Sander, C.; ...
Relationship between regional white matter hyperintensities and alpha oscillations in older adults
Neurobiology of Aging, 112 :1–11
Wilming, R.; Budding, C.; Müller, K. R.; Haufe, S.
Scrutinizing XAI using linear ground-truth data with suppressor variables
Machine Learning, Special Issue of the ECML PKDD 2022 Journal Track :1–21
Cai, C.; Hinkley, L.; Gao, Y.; Hashemi, A.; Haufe, S.; Sekihara, K.; Nagarajan, S. S.
Empirical Bayesian localization of event-related time-frequency neural activity dynamics
NeuroImage, 258 :119369–119369


Lichtner, G.; Balzer, F.; Haufe, S.; Giesa, N.; Schiefenhövel, F.; Schmieding, M.; ...
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia
Scientific Reports, 11 (1) :1–10
Hashemi, A.; Cai, C.; Kutyniok, G.; Müller, K. R.; Nagarajan, S. S.; Haufe, S.
Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework
NeuroImage, 239 :118309–118309
Popov, T.; Tröndle, M.; Barańczuk-Turska, Z.; Pfeiffer, C.; Haufe, S.; Langer, N.
Test–retest reliability of resting-state EEG in young and elderly adults
University of Zurich
Cai, C.; Hashemi, A.; Diwakar, M.; Haufe, S.; Sekihara, K.; Nagarajan, S. S.
Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm
NeuroImage, 225 :117411–117411
Hashemi, A.; Cai, C.; Gao, Y.; Ghosh, S.; Müller, K. R.; Nagarajan, S. S.; Haufe, S.
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
Haufe, S.
Poor Reconstruction of Complex Network Measures From M/EEG
International Journal of Psychophysiology 168, S, 87
Oala, L.; Murchison, A. G.; Balachandran, P.; Choudhary, S.; Fehr, J.; Leite, A. W.; ...
Machine learning for health: algorithm auditing & quality control
Journal of medical systems, 45 (12) :1–8
Budding, C.; Eitel, F.; Ritter, K.; Haufe, S.
Evaluating saliency methods on artificial data with different background types
arXiv preprint arXiv:2112., 4882
Hashemi, A.; Gao, Y.; Cai, C.; Ghosh, S.; Müller, K. R.; Nagarajan, S.; Haufe, S.
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Advances in Neural Information Processing Systems, 34 :24855–24870
Cesnaite, E.; Steinfath, P.; Idaji, M. J.; Stephani, T.; Kumral, D.; Haufe, S.; ...
Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age


Nentwich, M.; Ai, L.; Madsen, J.; Telesford, Q. K.; Haufe, S.; Milham, M. P.; Parra, L. C.
Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI
NeuroImage, 218 :117001–117001
Vidaurre, C.; Haufe, S.; Jorajuría, T.; Müller, K. R.; Nikulin, V. V.
Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance
Frontiers in Neuroscience, 14 :575081–575081
Stephani, T.; Waterstraat, G.; Haufe, S.; Curio, G.; Villringer, A.; VV ; Nikulin
Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses
The Journal of Neuroscience, 40 (34) :6572–6583


Schneck, N.; Tu, T.; Haufe, S.; Bonanno, G. A.; GalfaIvy, H.; Ochsner, K. N.; ...
Ongoing monitoring of mindwandering in avoidant grief through cortico-basal-ganglia interactions
Social cognitive and affective neuroscience, 14 (2) :163–172
Anzolin, A.; Presti, P.; Steen, F. Van De; Astolfi, L.; Haufe, S.; Marinazzo, D.
Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources
Brain topography, 32 (4) :655–674
Parra, L. C.; Haufe, S.; Dmochowski, J. P.
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data
NBDT, 2 (1)

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