Marco Kryda, M.Sc.

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Curriculum Vitae

  • Since 2019 | Ph.D. Student at Engineering Risk Analysis Group, Technical University of Munich
  • 2017 (Aug - Dec) | Exchange Semester, Nanyang Technological University (Singapore)
  • 2016 - 2019 | M.Sc. Applied and Engineering Physics, Technical University of Munich
  • 2015 (Aug - Dec) | Exchange Semester, Hong Kong University of Science and Technology
  • 2015 (Feb - Aug) | Internship Production Planning, Dr. Ing. h.c. F. Porsche AG (Stuttgart, Germany)
  • 2013 - 2016 | B.Sc. Physics, Technical University of Munich


  • Learning the reliability of perception sensors in autonomous driving from fleet-based data
  • Definition of environment perception errors
  • Development of stochastic models for sensor dependence


  • SS 20: Teaching assistant for the lecture “ Reliability of Engineering Systems ”, TUM.
  • WS19/20: Teaching assistant for the lecture “Risk Analysis”, TUM.

Supervised theses/projects

  • Master's thesis: Dominik Hesping (2020) - Construction and Evaluation of Machine Learning Models for Predicting a Multivariate Probability Distribution of Design Soil Parameters


  • M. Kryda, M. Qiu, M. Berk, B. Buschardt, T. Antesberger, R. German, D. Straub (2021). Associating sensor data and reference truth labels: A step towards SOTIF validation of perception sensors. Sixth IEEE International Workshop on Automotive Reliability, Test and Safety (ARTS).

  • M. Qiu, M. Kryda, F. Bock, T. Antesberger, D. Straub, R. German (2021). Parameter tuning for a Markov-based multi-sensor system. Euromicro SEAA 2021.
  • M. Kryda, M. Berk, B. Buschardt, and D. Straub (2021). Application of a statistical approach to assess the sensor perception reliabilities of automated driving vehicles. SAE WCX World Congress Experience Digital Summit.
  • M. Hauke, D. Weidlich, C. Ganter, D. Karampinos (2019). Insights from the Configuration Model theory accelerate Bloch simulations for dictionary-based T2 mapping. ISMRM 27th Annual Meeting & Exhibition, Montreal, Canada.