Hasso-Plattner-Institut
Prof. Dr. h.c. mult. Hasso Plattner
 

Johannes Huegle

Research Assistant, PhD Student

Phone:+49 (331) 5509-1320
Email:johannes.huegle(at)hpi.de
Address:August-Bebel-Str. 88, 14482 Potsdam
Room:V-2.05 (Campus II)
Profiles:ResearchGate, Google ScholarXING, LinkedIn

Research

Causal Structure Learning from Mixed and Nonlinear Data

Causal structure learning (CSL), i.e., the derivation of the causal graphical model representing the functional relationships between variables of a system from observational data, is crucial to many domains. However, while current methods of CSL assume the same data type or a specific family of functional relationships, most real-world scenarios incorporate heterogeneous data, e.g., mixed data. Moreover, as the underlying functional causal model and corresponding consistent model-based dependence measures are mostly unknown in advance the selection of inappropriate methods for CSL yields incorrect results.

As part of our research on Data-Driven Causal Inference we address these challenges through the introduction of information theoretic statistical methods and the development of a modular pipeline for experimental evaluation of CSL from observational data. We demonstrate the applicability of our approaches on both synthetic and real-world settings with regard to the consistency of the estimated underlying causal structures.

Keywords: Causal Structure Learning, Causal Inference, Machine Learning in Real-World Scenarios, Computational Statistics

Current Projects

  • Data-Driven Causal Inference
    • Information-Theoretic Causal Structure Learning from Mixed and Nonlinear Data
    • MPCSL - A Modular Pipeline for Causal Structure Learning
  • Application Scenarios with Cooperation Partners
    • Mechanical Engineering
    • Automotive Production

Teaching Activities

Lectures:

  • Causal Inference - Theory and Applications in Enterprise Computing (SS18, SS19, SS20)
  • Trends and Concepts in the Software Industry I (SS17)
  • Trends and Concepts in the Software Industry II (WS16/17)

Seminars:

Master Projects:

  • GPU-Accelerated Causal Structure Learning (WS21/22)
  • A Benchmark Suite for Causal Inference or “How to model a complex world?” (WS20/21)
  • Causal Reasoning on Enterprise Data (WS19/20)
  • Design and Implementation of a Causal Inference Pipeline (WS18/19)

Bachelor Projects:

(Co-) Supervised Master's Theses:

  • Causal Structure Learning for Car Manufacturing using Background Knowledge (Tobias Nack)
  • Parallel Execution Strategies for Constraint-Based Causal Structure Learning with an Entropy-Based Conditional Independence Test (Constantin Lange)
  • Constraint-based Causal Structure Learning of Gene Regulatory Networks (Philipp Bode)
  • An Entropy-Based Conditional Independence Test for Causal Structure Learning from Heterogeneous Data (Daniel Thevessen)
  • A Scalable GPU-Accelerated Causal Structure Learning Algorithm (Siegfried Horschig)

 

Education

Master's Degree in Mathematics, Heidelberg University, 2017, Focus: Statistics and Probability Theory, Thesis: "Asymptotics for Directed Interaction Networks based on Counting Process Theory"

Bachelor's Degree in Mathematics, Heidelberg University, 2014, Focus: Statistics and Probability Theory, Thesis "Pricing of Derivatives in the Binomial Model"

Publications

  • 1.
    Hagedorn, C., Huegle, J., Schlosser, R.: Understanding Unforeseen Production Downtimes in Manufacturing Processes using Log Data-driven Causal Reasoning. Journal of Intelligent Manufacturing. (2022).
     
  • 2.
    Huegle, J., Hagedorn, C., Boehme, L., Poerschke, M., Umland, J., Schlosser, R.: MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. WHY-21 @ NeurIPS 2021 (2021).
     
  • 3.
    Hagedorn, C., Huegle, J.: Constraint-Based Causal Structure Learning in Multi-GPU Environments. In: Seidl, T., Fromm, M., and Obermeier, S. (eds.) Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. pp. 106–118. CEUR-WS.org (2021).
     
  • 4.
    Huegle, J.: An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. pp. 4891–4892. International Joint Conferences on Artificial Intelligence Organization (2021).
     
  • 5.
    Huegle, J., Hagedorn, C., Perscheid, M., Plattner, H.: MPCSL - A Modular Pipeline for Causal Structure Learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 3068–3076. Association for Computing Machinery, New York, NY, USA (2021).
     
  • 6.
    Hagedorn, C., Huegle, J.: GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). pp. 37–45 (2021).
     
  • 7.
    Huegle, J., Hagedorn, C., Uflacker, M.: Unterstützte Fehlerbehebung durch kausales Strukturwissen in Überwachungssystemen der Automobilfertigung. In: Götz, S., Linsbauer, L., Schaefer, I., and Wortmann, A. (eds.) Software Engineering 2021 Satellite Events, Lecture Notes in Informatics (LNI). Gesellschaft für Informatik, Bonn (2021).
     
  • 8.
    Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. In: Felderer, M., Hasselbring, W., Rabiser, R., and Jung, R. (eds.) Software Engineering 2020. pp. 73–74. Gesellschaft für Informatik e.V., Bonn (2020).
     
  • 9.
    Huegle, J., Hagedorn, C., Uflacker, M.: How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 5246–5248. International Joint Conferences on Artificial Intelligence Organization (2020).
     
  • 10.
    Schmidt, C., Huegle, J.: Towards a GPU-Accelerated Causal Inference. HPI Future SOC Lab - Proceedings 2017. pp. 187–194 (2020).
     
  • 11.
    Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning. Algorithms and Architectures for Parallel Processing. ICA3PP 2019. pp. 89–104. Springer International Publishing (2020).
     
  • 12.
    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. pp. 59–77 (2019).
     
  • 13.
    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).
     
  • 14.
    Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). pp. 158–169. IEEE (2019).
     
  • 15.
    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. pp. 19:1–19:10. ACM, New York, NY, USA (2018).