CURRICULUM VITAE

Dr. MARIO MICHAEL KRELL

SUMMARY

  • Principal Machine Learning Engineer with 10-year background in machine learning
  • Strong problem solving, mathematical analysis, and interdisciplinary teamwork skills
  • Interested in leading applied research for interesting applications that help humanity


PERSONAL INFORMATION

contact:krell at uni-bremen dot de, hobbies choir singing, ballroom dancing, walking, cycling
languages:English (fluent), German (mother tongue), French (basic)


EDUCATION

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


SKILLS

  • problem solving, machine learning expertise, mathematical analysis, optimization
  • independent research and data analysis, research writing, teaching, scientific presentation
  • basic knowledge in robotics, man-machine interfaces, electroencephalography, multimedia, cars
  • Python programming, reStructuredText, Sphinx, YAML, Git, HPC, deep learning with Keras, scikit-learn (see pySPACE); basic experience with Solr, AWS, webpage development, and PySpark on Azure,
  • collaboration in multicultural/interdisciplinary teams (engineers, computer/neuro-scientists, manager, PO)
  • leadership experience with small teams, stakeholder interaction, certified scrum master/PO
  • project acquisition, patenting, documentation, process definition


SHORT WORK SUMMARY

Time Title Employer Reference
since 01/18 Principal Machine Learning Engineer Mercedes-Benz R&D, USA R Smiroldo
02/17 - 12/17 Postdoc UC Berkeley, ICSI, USA G Friedland
05/15 - 01/17 Postdoc and Senior Scientist University of Bremen, GER F&E Kirchner
07/10 - 04/15 Scientist University of Bremen, GER S Straube
05/09 - 06/10 Junior Scientist DFKI GmbH, Bremen, GER A Seeland


AWARDS AND GRANTS

2017:DAAD research scholarship for a project at ICSI, Berkeley
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
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)


WORK EXPERIENCE

since 01/18:

Principal Machine Learning Engineer at Mercedes-Benz Research & Development North America in the Statistics, Optimization, Machine Learning, and Analytics team (SOMA), Head: R Smiroldo

  • development of algorithms for user action prediction for the headunit in the car
  • big car data analytics to understand costumer behaviour and develop new products
  • patents (10 proposals), quality control, booth duty at CES
02/17-12/17:

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

05/15-01/17:

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

07/10-04/15:

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

  • general concepts for connecting SVM variants (regression, one-class classification, online learning) to improve understanding especially for teaching and usability
  • lead developer of pySPACE (open source release, refactoring, documentation, user support, user interface, multi-class, regression, pipeline decoding visualization, etc.)
  • contribution to project proposals and supervision of student assistants and a master thesis
  • successfully finished the project IMMI (intelligent man-machine interface)
05/09-06/10:

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

  • classification, performance evaluation, etc. added to pySPACE in project VI-Bot


RESEARCH TOPICS

Multimedia Big Data Studies:
 My objective was 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.


MAJOR PUBLICATIONS


UNIVERSITY TEACHING

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


MINOR PUBLICATIONS


PRESENTATIONS AND WORKSHOPS

  • 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


REVIEWING

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


OTHER PUBLICATIONS