Contact

Roman Rischke

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Teaching and research areas
Functions
Courses
Short vita
Memberships
Current publications

Teaching and research areas

  • Data Science
  • Applied mathematics
  • Machine learning
  • Optimization
  • Statistics

Functions

Outside the university

  • Membership of the Hochschullehrerbund (HLB)
  • Reviewer for various scientific journals and conferences

Courses

  • Data Mining
  • Advanced Data Mining
  • Software Engineering
  • Software requirements and modeling

Short vita

  • Since 02/2023: Professorship for Data Science, Coburg University of Applied Sciences
  • 04/2022 – 03/2023: Lecturer, Departments 3 and 4, HTW Berlin
  • 04/2017 – 01/2023: Postdoctoral Researcher and Project Manager, Research Group for Efficient Deep Learning in the Department of Artificial Intelligence & Research Group for Video Coding in the Department of Video Coding and Machine Learning, Fraunhofer Heinrich Hertz Institute Berlin
  • 09/2015 – 08/2016: Research Assistant, Discrete Mathematics Group at the Chair of Applied Geometry and Discrete Mathematics, Faculty of Mathematics, TU Munich
  • 09/2012 – 08/2015: Research Assistant, Working Group for Combinatorial Optimization and Graph Algorithms at the Faculty of Mathematics, TU Berlin
  • 10/2009 – 08/2012: Study of Business Mathematics (M.Sc.), TU Berlin
  • 10/2006 – 09/2009: Study of Business Mathematics (B.Sc.), TU Bergakademie Freiberg

You can find a detailed CV here.

Memberships

  • German Informatics Society (GI)
  • Association for Computing Machinery (ACM)
  • Institute of Electrical and Electronics Engineers (IEEE), Computer and Education Society
  • Teaching Software Engineering (LeSE, deputy chair since 2013)

Current publications

  • M. Ring, D. Schlör, S. Wunderlich, D. Landes, A. Hotho: Malware Detection on Windows Audit Logs Using LSTMs.
    In Computers and Security 109, https://doi.org/10.1016/j.cose.2021.102389, 2021, 1-12.
  • A. Schwarzmann, D. Landes, Y. Sedelmaier: About the Effectiveness of Different Game Design Elements for an Introductory Programming Course.
    In M.E. Auer and T. Rüütmann (eds.): Educating Engineers for Future Industrial Revolutions, Advances in Intelligent Systems and Computing 1328.
    Springer, Cham, https://doi.org/10.1007/978-3-030-68198-2_46, 2021, 499-509.
  • Y. Sedelmaier, D. Landes, E. Erculei: How to Design a Competence-Oriented Study Program for Data Scientists?
    In Proc.
    12th IEEE Global Engineering Education Conference (EDUCON 2021), Vienna, Austria, 2021, 1595-1599.
  • M. Klopp, C. Gold-Veerkamp, J. Abke, K. Borgeest, R. Reuter, S. Jahn, J. Mottok, Y. Sedelmaier, A. Lehmann, D. Landes: Totally Different and yet so Alike: Three Concepts to Use Scrum in Higher Education.
    In Proc.
    4th European Conference on Software Engineering Education (ECSEE’20), 2020, 12-21.
  • R. Reuter, T. Stark, Y. Sedelmaier, D. Landes, J. Mottok, C. Wolff: Insights into Students’ Problems during UML Modeling.
    In Proc.
    11th IEEE Global Engineering Education Conference (EDUCON 2020), Porto, Portugal, 2020, 592-600.
  • A. Schwarzmann, D. Landes, Y. Sedelmaier: About the Effectiveness of Different Game Design Elements for an Introductory Programming Course.
    In Proc.
    23rd International Conference on Interactive Collaborative Learning (ICL2020), 2020, 552-562.
  • Y. Sedelmaier, D. Landes: Analyzing Challenges in Software Engineering Capstone Projects.
    In Proc.
    15th International Conference on Software Engineering Advances (ICSEA2020), Porto, Portugal, 2020, 135-140, available online at https://www.thinkmind.org/index.php?view=article&articleid=icsea_2020_1_200_10100.
  • Y. Sedelmaier, D. Landes: Nine years of EVELIN – findings and perspectives.
    In Didaktiknachrichten 12/2020, DiZ- Center for University Didactics, Ingolstadt, 2020, 32-39.
  • N. Waibel, Y. Sedelmaier, D. Landes: Using Learning Styles to Accommodate for Heterogeneous Groups of Learners in Software Engineering.
    In Proc.
    11th IEEE Global Engineering Education Conference (EDUCON 2020), Porto, Portugal, 2020, 819-826.
  • M. Wolf, M. Ring, D. Landes: Impact of Generative Adversarial Networks on NetFlow-Based Traffic Classification.
    In Á.
    Herrero, C. Cambra, D. Urda, J. Sedano and E. Corchado (Eds.): 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), Advances in Intelligent Systems and Computing 1276, Springer, Cham, 2020, 393-404.
  • S. Wunderlich, M. Ring, D. Landes, A. Hotho: The Impact of Different System Call Representations on Intrusion Detection.
    In Logic Journal of the IGPL, 2020, https://doi.org/10.1093/jigpal/jzaa058.