Alireza Furutanpey On Leave
Univ.Ass. Dipl.-Ing. / BSc
Role
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PreDoc Researcher
Distributed Systems, E194-02
Courses
2024W
- Advanced Internet Computing / 184.269 / VU
- Distributed Systems / 194.024 / VU
- Project in Computer Science 1 / 194.145 / PR
- Project in Computer Science 2 / 194.146 / PR
- Seminar in Distributed Systems / 184.194 / SE
2025S
- Project in Computer Science 1 / 194.145 / PR
- Project in Computer Science 2 / 194.146 / PR
Publications
- Efficient Generative Modelling for Transmitting Salient Information / Furutanpey, A. (2024, June 26). Efficient Generative Modelling for Transmitting Salient Information [Poster Presentation]. Generative Modeling Summer School (GeMSS 2024), Eindhoven, Netherlands (the).
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faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms
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Raith, P. A., Rausch, T., Furutanpey, A., & Dustdar, S. (2023). faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms. Software: Practice and Experience, 53(12), 2327–2361. https://doi.org/10.1002/spe.3277
Download: PDF (4.71 MB) - Architectural Vision for Quantum Computing in the Edge-Cloud Continuum / Furutanpey, A., Barzen, J., Bechtold, M., Dustdar, S., Leymann, F., Raith, P., & Truger, F. (2023). Architectural Vision for Quantum Computing in the Edge-Cloud Continuum. In S. Ali, C. Ardagna, N. Atukorala, J. Barzen, C. K. Chang, Chang Rong N., J. Fan, I. Faro, S. Feld, G. Fox, Z. Jin, F. Leymann, F. Neukart, S. de la Puente, & M. Wimmer (Eds.), Proceedings of the IEEE International Conference on Quantum Software (IEEE QSW 2023) (pp. 88–103). IEEE. https://doi.org/10.1109/QSW59989.2023.00021
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Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
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Furutanpey, A., Barzen, J., Bechtold, M., Dustdar, S., Leymann, F., Raith, P. A., & Truger, F. (2023). Architectural Vision for Quantum Computing in the Edge-Cloud Continuum. arXiv. https://doi.org/10.34726/5940
Download: PDF (3.23 MB) -
FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing
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Furutanpey, A., Raith, P. A., & Dustdar, S. (2023). FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing. arXiv. https://doi.org/10.48550/arXiv.2302.10681
Download: PDF (3.65 MB) -
Adaptive and Collaborative Inference: Towards a No-compromise Framework for Distributed Intelligent Systems
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Furutanpey, A., & Dustdar, S. (2022). Adaptive and Collaborative Inference: Towards a No-compromise Framework for Distributed Intelligent Systems. In S. Decker, F. J. Domínguez Mayo, M. Marchiori, & J. Filipe (Eds.), Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022) (pp. 144–151). SciTePress. https://doi.org/10.5220/0011547800003318
Download: Paper (489 KB) - An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques / Raith, P., Rausch, T., Prüller, P., Furutanpey, A., & Dustdar, S. (2022). An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques. In 2022 IEEE International Conference on Cloud Engineering (IC2E) (pp. 22–28). IEEE. https://doi.org/10.1109/IC2E55432.2022.00010
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Persistable, distributable and versionable application state for cloud service emulators
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Furutanpey, A. (2022). Persistable, distributable and versionable application state for cloud service emulators [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.96467
Download: PDF (3.84 MB) - A Serverless Computing Fabric for Edge & Cloud / Nastic, S., Raith, P., Furutanpey, A., Pusztai, T., & Dustdar, S. (2022). A Serverless Computing Fabric for Edge & Cloud. In Proceedings 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) (pp. 1–12). IEEE. https://doi.org/10.1109/CogMI56440.2022.00011
Supervisions
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Enhancing Image Retrieval Re-Ranking using Mutual Information Minimization
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Seidl, M. (2024). Enhancing Image Retrieval Re-Ranking using Mutual Information Minimization [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.124402
Download: PDF (2.33 MB) -
Lossless neural compression for resource constrained environments: Using deep latent probabilistic models with bits-back asymmetric numerical systems
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Bauernfeind, F. (2023). Lossless neural compression for resource constrained environments: Using deep latent probabilistic models with bits-back asymmetric numerical systems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.107248
Download: PDF (5.5 MB)