Johannes Huegle

Research Assistant, PhD Student

	Phone:	+49 (331) 5509-1320
	Fax:	+49 (331) 5509-579
	Email:	johannes.huegle(at)hpi.de
	Address:	August-Bebel-Str. 88, 14482 Potsdam
	Room:	V-2.05 (Campus II)
	Profiles:	XING, LinkedIn

Research

Causal Structure Learning from Heterogeneous 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 to 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.

Publications

Schmidt, C., Huegle, J.: Towards a GPU-Accelerated Causal Inference.HPI Future SOC Lab - Proceedings 2017. pp. 187-194 (2020).
[ BibTeX ]

@inproceedings{schmidt2020towards, author = {Schmidt, Christopher and Huegle, Johannes}, booktitle = {HPI Future SOC Lab - Proceedings 2017}, keywords = {gpu_acceleration causal_inference}, pages = {187-194}, title = {Towards a GPU-Accelerated Causal Inference}, year = 2020 }
Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning.Algorithms and Architectures for Parallel Processing. ICA3PP 2019. p. 89--104. Springer International Publishing (2020).
[ BibTeX ]

@inproceedings{schmidt2020outofcore, author = {Schmidt, Christopher and Huegle, Johannes and Horschig, Siegfried and Uflacker, Matthias}, booktitle = {Algorithms and Architectures for Parallel Processing. ICA3PP 2019}, keywords = {gpu_acceleration causal_inference}, pages = {89--104}, publisher = {Springer International Publishing}, title = {Out-of-Core GPU-Accelerated Causal Structure Learning}, year = 2020 }
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).
[ BibTeX ]

@inproceedings{schmidt2019loadbalanced, author = {Schmidt, Christopher and Huegle, Johannes and Bode, Philipp and Uflacker, Matthias}, booktitle = {SIGKDD Workshop on Causal Discovery}, keywords = {causal_inference}, pages = {59--77}, series = {Proceedings of Machine Learning Research}, title = {Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data}, volume = 104, year = 2019 }
Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems.2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). pp. 1381-1392 (2019).
[ BibTeX ] [ Download ]

@inproceedings{hesse2019quantitative, abstract = {With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark Streaming, Apache Flink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.}, author = {Hesse, Guenter and Matthies, Christoph and Glass, Kelvin and Huegle, Johannes and Uflacker, Matthias}, booktitle = {2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)}, keywords = {imported esb data_stream_processing}, pages = {1381-1392}, title = {Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems}, year = 2019 }
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).
[ BibTeX ] [ Download ]

@inproceedings{matthies2019ICSE, abstract = {The perceptions and attitudes of developers impact how software projects are run and which development practices are employed in development teams. Recent agile methodologies have taken this into account, focusing on collaboration and shared team culture. In this research, we investigate the perceptions of agile development practices and their usage in Scrum software development teams. Although perceptions collected through surveys of 42 participating students did not evolve significantly over time, our analyses show that the Scrum role significantly impacted participants' views of employed development practices. We find that using the version control system according to agile ideas was consistently rated most related to the values of the Agile Manifesto. Furthermore, we investigate how common software development artifacts can be used to gain insights into team behavior and present the development data measurements we employed. We show that we can reliably detect well-defined agile practices, such Test-Driven Development, in this data and that usage of these practices coincided with participants' self-assessments.}, author = {Matthies, Christoph and Huegle, Johannes and Dürschmid, Tobias and Teusner, Ralf}, booktitle = {2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)}, keywords = {software_engineering Scrum metrics Agile}, month = {may}, pages = {158-169}, publisher = {IEEE}, title = {Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices}, year = 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).
[ BibTeX ] [ Download ]

@inproceedings{schmidt2018orderindependent, address = {New York, NY, USA}, author = {Schmidt, Christopher and Huegle, Johannes and Uflacker, Matthias}, booktitle = {SSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management}, keywords = {gpu_acceleration causal_inference}, pages = {19:1--19:10}, publisher = {ACM}, series = {SSDBM '18}, title = {Order-independent constraint-based causal structure learning for gaussian distribution models using GPUs}, year = 2018 }

Johannes Huegle

Research Assistant, PhD Student

Research

Causal Structure Learning from Heterogeneous Data

Publications

Chair of Prof. Hasso Plattner

News

03.03.2020 | Best Master Thesis in Education Technologies 2019

21.11.2019 | Sugar Fall Presentation @ HPI

14.10.2019 | 2019 Bachelor's Project featured in SAP TechEd Keynote

23.07.2019 | Presentation of 2019 Bachelor's Project

12.06.2019 | Global Team-based Innovation Final Presentations

25.02.2019 | Best Paper Award received at ICORES 2019

Open Positions

Literature