Prof. Dr. h.c. Hasso Plattner

Johannes Huegle

Research Assistant, PhD Student

  Phone: +49 (331) 5509-1320
  Fax: +49 (331) 5509-579
  Email: johannes.huegle(at)hpi.de
  Room: V-2.05 (Campus II)

Research Interests

  • Causal Inference
  • Machine Learning 
  • Computational Statistics
  • Graphical Models
  • Statistical Hypothesis Testing

Research Project

In the recent years, causality has grown from a nebulous concept into a mathematical theory based on the concept of probabilstic graphical modeling. Nevertheless, the complexity of the theoretical concepts and the requirements of current algorithms hinders the application of causal inference in practise. Therefore,  our research project Data-Driven Causal Inference adresses these challenges by improvements in both the application of statistical concepts and the acceleration of hard- and software.


Master's degree in Mathematics, Heidelberg University, 2017

  • Main emphasis: statistics and probability theory
  • Field of application: economics
  • Thesis: "Asymptotics for Directed Interaction Networks based on Counting Process Theory"

Bachelor's degree in Mathematics, Heidelberg University, 2014

  • Main emphasis: statistics and probability theory
  • Field of application: economics
  • Thesis: "Pricing of Derivatives in the Binomial Model"


  • Schmidt, C., Huegle, J., Bode, P., Uflacker, M.: Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. SIGKDD Workshop on Causal Discovery. p. 59--77 (2019).
  • Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems. IEEE International Conference on Distributed Computing Systems (ICDCS). pp. 1381-1392 (2019).
  • Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. Proceedings of the 41st International Conference on Software Engineering Companion (ICSE'19) (2019).
  • Schmidt, C., Huegle, J., Uflacker, M.: Order-independent constraint-based causal structure learning for gaussian distribution models using GPUs. SSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management. p. 19:1--19:10. ACM, New York, NY, USA (2018).