contact:krell at uni-bremen dot de, languages German (mother tongue), English (fluent)
born:Germany, Frankfurt (Oder), 1984, hobbies choir singing, ballroom dancing, walking, cycling


  • Postdoc (senior scientist) with 8-year background in machine learning and data analysis
  • Strong problem solving, mathematical analysis, and interdisciplinary teamwork skills
  • Interested in leading applied research in machine learning for interesting applications that help humanity


Time Degree Institute Advisor GPA
03/15 PhD in machine learning University of Bremen, Robotics Group Kirchner, F. 4.0
03/09 degree in mathematics (“Diplom”) Humboldt University of Berlin Kummer, B. 3.9
07/06 pre-degree in computer science Humboldt University of Berlin   3.8
06/03 university-entrance diploma C.-F.-Gauß-Gymnasium Frankfurt (O.)   3.8


  • problem solving, machine learning expertise, mathematical analysis, optimization
  • independent research and data analysis, research writing, teaching, scientific presentation
  • collaboration in multicultural/interdisciplinary teams (engineers, computer/neuro-scientists, manager)
  • basic knowledge in robotics, man-machine interfaces, electroencephalographic data, and multimedia data
  • project acquisition, basic experience with Solr, AWS, and webpage development:
  • Python programming, reStructuredText, Sphinx, YAML, OmniGraffle, Git, slurm, HPC, deep learning with Keras, scikit-learn (see pySPACE - my open source machine learning framework)


2017:Lead DFKI activity for H2020 Grant (InFuse), 3.5 Mio. Euro
2017:Industry project funding by local government (xMove), 200.000 Euro
2017:Second prize for best student poster at OCEANS 2017 MTS/IEEE Aberdeen
2017:DAAD research scholarship for a project at ICSI, Berkeley
2016:YERUN scholarship for Big Data and Analytics Summer School at the University of Essex
2015:Scholarship of University of Bremen for 29th Machine Learning Summer School, Kyoto
2010:Contributed to federal government grant (IMMI), 3 Mio. Euro
2005-2009:Scholarship of Hans-Böckler Stiftung (Hans Böckler Foundation)


  • pySPACE workshop (2015), DL workshop (2016), ML workshop (2016), DFKI RIC, Bremen, Germany
  • Representation of the DFKI RIC at the CeBIT international computer expo (2015), Hannover, Germany
  • Introduction to pySPACE (2014), PyData Berlin 2014, Berlin, Germany
  • Our Tools for Large Scale or Embedded Processing of Physiological Data (2014), Passive BCI Community Meeting, Delmenhorst, Germany
  • Introduction to pySPACE workflows (2013), NIPS workshop Machine Learning Open Source Software: Towards Open Workflows, Lake Tahoe, Nevada, USA


since 01/18:

Principal Data Scientist at Mercedes-Benz Research & Development North America


Postdoctoral Research Scholar at ICSI (International Computer Science Institute), University of California Berkeley, Supervisor: Gerald Friedland


Sr. Scientist at the Robotics Group, University of Bremen, Head: F Kirchner


Scientist at the Robotics Group, University of Bremen, Head: F Kirchner

  • successfully finished the project IMMI (intelligent man-machine interface)
  • general concepts for connecting SVM variants ((regression, one-class classification, online learning)) to improve understanding especially for teaching and usability
  • backtransformation (new visualization concept for signal processing chains)
  • lead developer of pySPACE (open source release, refactoring, documentation, user support, user interface, multi-class, regression, etc.)
  • contribution to project proposals and supervision of student assistants and a master thesis

Jr. Scientist at the DFKI GmbH (German Research Center for Artificial Intelligence), Robotics Innovation Center, Bremen, Head: F Kirchner

  • contributed to the project VI-Bot and the project proposal of IMMI
  • classification, performance evaluation, etc. added to pySPACE and code optimization
  • mathematical model for space simulation in the project Inveritas


Semester Type Title Organizer
FA2017 seminar Undergrad. Research Apprentice Program (G Friedland)
SP2017 seminar Undergrad. Research Apprentice Program (G Friedland)
WS2016 seminar decision models in natural sciences HG Döbereiner
WS2016 complete lecture machine learning for autonomous robots (F Kirchner)
SS2016 lecture+tutorial reinforcement learning F Kirchner
WS2015 complete lecture machine learning for autonomous robots (F Kirchner)
SS2015 lecture reinforcement learning F Kirchner
SS2015 corrected exams fundamentals in computer science 2 F Kirchner
WS2014 coordination behaviour based robotics F Kirchner
WS2014 lecture+coord. machine learning for autonomous robots (F Kirchner)
WS2013 lecture+tutorial machine learning for autonomous robots F Kirchner
SS2012 tutorial analysis 2 (mathematics) B Stratmann
WS2011 tutorial analysis 1 (mathematics) B Stratmann
SS2010 tutorial mathematics 2 (computer science) R Stöver
WS2009 tutorial mathematics 1 (computer science) R Stöver
before exercise sheets corrections for mathematics lectures Various



Pattern Recognition, Expert Systems with Applications, Information Sciences, Sensors, IEEE MultiMedia, ACM Multimedia, Chemometrics and Intelligent Laboratory Systems, Biomedical Signal Processing and Control, International Journal of Machine Learning and Cybernetics, Neural Computing and Applications, Recent Patents on Electrical & Electronic Engineering, Progress in Artificial Intelligence, Neuroadaptive Technology Conference, and internal group reviews


Multimedia Big Data Studies:
 My objective is to implement a framework that enables researchers of many research fields to extract useful data from user-generated content to perform field studies.
Framework - pySPACE:
 is a signal processing and classification environment written in Python which is supporting parallelization and intuitive configuration (based on YAML). I contributed the major parts to it like documentation, usability, numerous algorithms, evaluation, etc.
Support Vector Machines (SVMs):
 Due to their generalization capability on few data with high dimensions, the SVM is still a common classifier. I discovered (smooth) connections to linear discriminant analysis, support vector regression, relative margin machine, one-class SVM, and the online passive-aggressive algorithm. to improve the understanding of these algorithms.
Intelligent Man-Machine Interaction (IMMI):
 My task was to improve the electroencephalographic (EEG) data processing to detect the perception of rare infrequent important events or to predict upcoming movements.
Robotics:I supported colleagues in robotic applications like underwater vehicle movement modeling, reinforcement learning, soil detection, outlier detection, space simulation modeling, etc.