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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Modellgetriebene Software Entwicklung

M. Sc. Alexander Schultheiß

Mail Address:

Institut für Informatik
Humboldt-Universität zu Berlin
Unter den Linden 6
D-10099 Berlin

Visitor Address:

(Currently closed)
Johann-von-Neumann-Haus
Rudower Chaussee 25, Raum 4.212
D-12489 Berlin-Adlershof

VoIP:

Discord:

AlexS#1561

E-Mail:

 

alexander.schultheiss (at) informatik.hu-berlin.de

GitHub

https://github.com/
AlexanderSchultheiss

 

About Myself

I studied informatics at the Friedrich Schiller University Jena (Jena is a beautiful little town in Thuringia - the green heart of Germany - that is definitely worth a visit). During my studies, I focused on computer vision and software engineering. 

Since March 2019, I am a PhD student with Timo Kehrer at Humbold University of Berlin and part of the VariantSync project. My main research interests lie in the systematic support of multi-variant software development, version control, and improving the way in which we as a community conduct our scientific work. 

Publications

  • Timo Kehrer, Thomas Thüm, Alexander Schultheiß, Paul Maximilian Bittner, "Bridging the Gap Between Clone-and-Own and Software Product Lines", Proceedings of the International Conference on Software Engineering (ICSE 2021).
    http://dx.doi.org/10.18725/OPARU-35726
    You can find the ICSE talk on YouTube.
  • Alexander Schultheiß, Paul Maximilian Bittner, Timo Kehrer, and Thomas
    Thüm. 2020. On the Use of Product-Line Variants as Experimental Subjects
    for Clone-and-Own Research: A Case Study. In 24th ACM International
    Systems and Software Product Line Conference (SPLC ’20), October 19–23,
    2020, Montréal, QC, Canada. ACM, New York, NY, USA, 6 pages.
    https://doi.org/10.1145/3382025.3414972
  • Alexander Schultheiß, Alexander Boll, Timo Kehrer, “Comparison of Graph-based Model Transformation Rules”, Journal of Object Technology, Volume 19, no. 2 (July 2020), pp. 3:1-21, doi:10.5381/jot.2020.19.2.a3.
  • Schultheiss A., Käding C., Freytag A., Denzler J. (2017) Finding the Unknown: Novelty Detection with Extreme Value Signatures of Deep Neural Activations. In: Roth V., Vetter T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science, vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_19