CURRICULUM VITAE

Dr. MARIO MICHAEL KRELL

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


SUMMARY

  • Senior Machine Learning Researcher with 13-year of cumulative experience in many applications
  • Strong problem solving, mathematical analysis, interdisciplinary teamwork, and leadership skills (10 years)
  • Interested in leading applied research in the Bay Area that helps humanity


EDUCATION

Time Degree Institute Advisor GPA
2017 Postdoc UC Berkeley, ICSI, USA G Friedland  
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


SKILLS

  • problem solving, machine learning, deep learning, mathematical analysis, optimization
  • leadership experience with up to 8 direct reports, stakeholder interaction, and as scrum master/PO
  • collaboration in multicultural/interdisciplinary teams (engineers, computer/neuro-scientists, manager, PO)
  • independent research and data analysis (>40 publications, >500 citations), teaching, scientific presentation
  • software development: Python, NumPy, Git, Sphinx, HPC, TensorFlow, PyTorch, documentation, PySpark
  • GNN, SVM, SVR, CNN (ResNet-50), NLP (BERT), probabilistic models (ABC, AdGMoM), RL (MiniGo), evaluation, capacity, differential privacy, signal processing, clustering, source localisation
  • basic knowledge in brain-machine interfaces, robotics, multimedia, cars, hardware acceleration (IPUs)


SHORT WORK SUMMARY

Time Title Employer Reference
08/22 - 10/22 AI Engineering Manager Graphcore J Irwin
09/19 - 08/22 AI Applications Specialist Graphcore M Iyer
01/18 - 07/19 Principal ML/Data Scientist Mercedes-Benz R&D, USA H Endt
02/17 - 12/17 Postdoc UC Berkeley, ICSI, USA G Friedland
03/15 - 01/17 Postdoc and Senior ML Researcher University of Bremen, GER F&E Kirchner
07/10 - 03/15 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

2021:Top performer at Graphcore
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
Scholarships:Hans-Böckler Stiftung (2005-2009), University of Bremen (2015), Yerun (2016)


WORK EXPERIENCE

09/19-10/22:

Principal Machine Learning Lead at Graphcore

01/18-07/19:

Principal Data Scientist at Mercedes-Benz R&D North America in the Statistics, Optimization, Machine Learning, and Analytics (SOMA) team

  • 3 direct reports, 4 projects, many stakeholders, 10 patent proposals (5 filings), 2 papers
  • big car data analytics to understand customer behaviour and develop new products
  • development of algorithms for predicting driver interaction with the display in the car
02/17-12/17:

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

  • guide 5 undergraduate students for URAP, implement tools for performing big data studies in numerous different disciplines using the multimedia commons
  • deep learning: capacity analysis and processing pipeline development for EEG data
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

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


RECOMMENDATIONS

James Irwin (Director at Graphcore): “Mario faced the fire-hose fast-track of everything that would normally be exaggerated training examples. He must have grown a full foot in the course of his tenure as an AI engineering manager. A baptism of fire would have been a cake-walk compared to what was thrown at him. He did not flinch, and he did not cower. He did not dismiss as irrelevant & divert to HR truly important personal staff issues that immediately tested his values as a manager. Mario is empathetic and resourceful. He acted, showing he knew his role was to enable the team to be successful. A dazzlingly promising engineering manager. That’s the part that matters here since Mario’s undoubted credibility as an engineer will put a team at ease quickly.”

Phil Brown (VP at Graphcore): “Mario is fantastic to work with. In addition to a strong AI Engineering skill set he is extremely positive, proactive and dedicated, helping lift and enhance his team and colleagues. He made key contributions across a range of areas, including implementing and optimizing a reinforcement learning application with a novel execution scheme, driving our MLPerf submissions - particularly finding a series of significant optimizations on a key CNN benchmark, and working with a number of customers on new Graph Neural Networks with fantastic results. More recently Mario has transitioned successfully into team leadership, helping steer his team through organisational change whilst always keeping the group upbeat and engaged.”

Alexander Tsyplikhin (Sr. Manager): “I had the pleasure and honor to work with Mario at Graphcore. He has incredible technical depth, an ability to learn very quickly, and outstanding communication skills. He led multiple activities at the same time, ranging from MLperf submissions to research papers and customer projects. During his time as IC, he worked on a vast spectrum of ML models and domains: reinforcement learning, computer vision scaling optimization, audio processing, PDE solvers, fraud detection, differential privacy, approximate Bayesian computation, et. al. He was productive and efficient at every project, and he always had time to coach co-workers on technical and non-technical questions. Taking over the team as a manager was a natural change for him. Mario was amazing as a manager, keeping his engineers happy and engaged, including during periods of uncertainty. I will be happy to work with Mario in the future and can recommend him to anyone extremely highly.”


TEACHING

Semester Type Title Organizer
2019/20/21 seminar work topics related reading group MM Krell
2019 seminar discuss different ML and CS algorithm MM Krell
FA2017 seminar Undergrad. Research Apprentice Program (G Friedland)
SP2017 seminar Undergrad. Research Apprentice Program (G Friedland)
2014-17 seminar machine learning workgroup MM Krell
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


MAJOR PUBLICATIONS


PRESENTATIONS AND WORKSHOPS

  • Graphcore at Fürberg Workshop: Hybrid AI - combining symbolic, deep learning and neuromorphic (2022)
  • Software/hardware co-optimization on the IPU: An MLPerf™ case study (2021), Hot Chips 33 Symposium
  • 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

ICML, NeurIPS, 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 reviews


MINOR PUBLICATIONS


OTHER PUBLICATIONS