2023W

• AI/ML in the Era of Climate Change / 194.125 / VU
• Bachelor Thesis for Informatics and Business Informatics / 188.944 / PR

2024S

• Data-intensive Computing / 194.048 / VU

• An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge / Tundo, A., Mobilio, M., Ilager, S. S., Brandic, I., Bartocci, E., & Mariani, L. (2023). An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 281–293). IEEE. https://doi.org/10.1109/ASE56229.2023.00046
  Projects: Intrasafed 5G (2020–2021) / RUCON (2016–2023) / SWAIN (2021–2024) / TRITON FWF (2023–2027)
• Data-centric Edge-AI: A Symbolic Representation Use Case / Ilager, S. S., De Maio, V., Lujic, I., & Brandic, I. (2023). Data-centric Edge-AI: A Symbolic Representation Use Case. In 2023 IEEE International Conference on Edge Computing and Communications (EDGE) (pp. 301–308). IEEE. https://doi.org/10.1109/EDGE60047.2023.00052
• SymED: Adaptive and Online Symbolic Representation of Data on the Edge / Hofstätter, D., Ilager, S. S., Lujic, I., & Brandic, I. (2023). SymED: Adaptive and Online Symbolic Representation of Data on the Edge. In J. Cano, M. D. Dikaiakos, G. A. Papadopoulos, M. Pericàs, & R. Sakellariou (Eds.), Euro-Par 2023: Parallel Processing : 29th International Conference on Parallel and Distributed Computing, Limassol, Cyprus, August 28 – September 1, 2023, Proceedings (pp. 411–425). Springer. https://doi.org/10.1007/978-3-031-39698-4_28

• Data-driven Methods for Climate Change Modelling in Hydrology: A Use Case for Deep Learning in Rainfall-Runoff Simulation / Eder, M. (2024). Data-driven Methods for Climate Change Modelling in Hydrology: A Use Case for Deep Learning in Rainfall-Runoff Simulation [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.113024
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• Achieving sustainable federated edge analytics by using incomplete data / Maliakel, P. J. (2023). Achieving sustainable federated edge analytics by using incomplete data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.108307
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• A systematic evaluation of federated learning algorithms in the context of industrial applications / Pruckovskaja, V. (2023). A systematic evaluation of federated learning algorithms in the context of industrial applications [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.96643
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• Edge data management with symbolic representation / Kanatbekova, M. (2022). Edge data management with symbolic representation [Diploma Thesis, Technische Universität Wien; University of L’Aquila]. reposiTUm. https://doi.org/10.34726/hss.2022.106716
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