CURRICULUM VITAE

Dr. MARIO MICHAEL KRELL

SUMMARY

  • Senior Machine Learning Researcher with 10-year background in machine learning
  • Strong problem solving, mathematical analysis, and interdisciplinary teamwork skills
  • Interested in applied research in the Bay Area that helps humanity


PERSONAL INFORMATION

email krell at uni-bremen dot de hobbies choir singing, ballroom dancing, hiking, Zumba
address Redwood City, CA languages English (fluent), German (fluent), French (basic)


EDUCATION

Time Degree Institute Advisor GPA
03/15 PhD in CS (machine learning) University of Bremen, Robotics Group F Kirchner 4.0
03/09 degree in mathematics (“Diplom”) Humboldt University of Berlin B Kummer 3.9
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 (>35 publications, >350 citations), teaching, scientific presentation
  • basic knowledge in brain-machine interfaces, robotics, multimedia, cars, intelligent processing units (IPUs)
  • Software development in Python: NumPy, Git, Sphinx, HPC, scikit-learn (see pySPACE); basic experience with IPU, TensorFlow, PyTorch, Solr, AWS, data analytics with PySpark on Azure
  • collaboration in multicultural/interdisciplinary teams (engineers, computer/neuro-scientists, manager, PO)
  • leadership experience with small teams, stakeholder interaction, and as scrum master/PO
  • project acquisition, patenting, documentation, process definition


SHORT WORK SUMMARY

Time Title Employer Reference
since 09/19 AI Applications Specialist Graphcore P Brown
01/18 - 07/19 Principal Machine Learning Engineer Mercedes-Benz R&D, USA H Endt
02/17 - 12/17 Postdoc UC Berkeley, ICSI, USA G Friedland
05/15 - 01/17 Postdoc and Senior ML Researcher University of Bremen, GER F&E Kirchner
07/10 - 04/15 Machine Learning Researcher University of Bremen, GER S Straube
05/09 - 06/10 Jr. ML Researcher 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
2005-2009:Scholarship of Hans-Böckler Stiftung (Hans Böckler Foundation)


WORK EXPERIENCE

since 09/19:

AI Applications Specialist (Senior ML Engineer/Researcher) at Graphcore

01/18-07/19:

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

  • big car data analytics to understand customer behaviour and develop new products
  • development of algorithms for user action prediction for the headunit (display) in the car
  • stakeholder interaction, lead 4 different projects, project acquisition, quality control
  • 10 patent proposals (5 filings), 2 white papers, booth duty at CES & GTC
02/17-12/17:

Postdoctoral Research Scholar in Machine Learning at ICSI (International Computer Science Institute), University of California Berkeley

05/15-01/17:

Sr. Machine Learning Researcher at the Robotics Group, University of Bremen, and

07/10-04/15:

Machine Learning Researcher at the Robotics Group, University of Bremen

  • consulting in all ML projects, project acquisition, support of more than 4 projects
  • develop general concepts for connecting SVM variants (regression, one-class classification, online learning) to improve understanding especially for teaching and usability
  • lead ML software developer of pySPACE (open source release, refactoring, documentation, user support, user interface, multi-class, regression, pipeline decoding visualization, etc.)
  • organizer of the signal processing and the machine learning workgroups (around 20 people)
  • student supervision (online SVMs; automatic processing chain optimization)
05/09-06/10:Jr. ML Researcher at the DFKI GmbH, Robotics Innovation Center, Bremen


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


RESEARCH TOPICS

Intelligence Processing Unit (IPU):
 How can applications be accelerated by this novel chip and which algorithms fit best? I contributed applications, guides, and code.
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


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