TU Wien Informatics

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Vincenzo De Maio

Projektass. / PhD

Vincenzo De Maio

Role

Contact

2024W

 

  • Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing / De Maio, V., Kanatbekova, M., Zilk, F., Friis, N., Guggemos, T., & Brandic, I. (2024). Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing. In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 626–635). https://doi.org/10.1109/CCGrid59990.2024.00075
    Projects: HPQC (2023–2025) / RUCON (2016–2023) / 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
  • International Conference on High Performance Computing / De Maio, V., & Brandic, I. (Eds.). (2023). International Conference on High Performance Computing (Vol. 13999). Springer. https://doi.org/10.1007/978-3-031-40843-4
  • Accelerating Scientific Applications with the Quantum Edge: A Drug Design Use Case / De Maio, V., & Brandic, I. (2023). Accelerating Scientific Applications with the Quantum Edge: A Drug Design Use Case. In A. Bienz, M. Weiland, M. Baboulin, & C. Kruse (Eds.), High Performance Computing : ISC High Performance 2023 International Workshops, Hamburg, Germany, May 21–25, 2023, Revised Selected Papers (pp. 134–143). Springer. https://doi.org/10.1007/978-3-031-40843-4_11
  • Sustainable Environmental Monitoring via Energy and Information Efficient Multi-Node Placement / Ahmad, S., Uyanık, H., Ovatman, T., Sandıkkaya, M. T., Maio, V. D., Brandić, I., & Aral, A. (2023). Sustainable Environmental Monitoring via Energy and Information Efficient Multi-Node Placement. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3303124
  • Distributed systems in the Post-Moore era / De Maio, V. (2023, March 14). Distributed systems in the Post-Moore era [Presentation]. TEWI Kolloquium of the University of Klagenfurt, 9020 Klagenfurt, Austria.
  • A Roadmap To Post-Moore Era for Distributed Systems / De Maio, V., Aral, A., & Brandic, I. (2022). A Roadmap To Post-Moore Era for Distributed Systems. In ApPLIED ’22: Proceedings of the 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems (pp. 30–34). Association for Computing Machinery (ACM). https://doi.org/10.1145/3524053.3542747
    Download: Artikel (2.5 MB)
    Projects: RUCON (2016–2023) / SWAIN (2021–2024)
  • Edge offloading for microservice architectures / Zilic, J., De Maio, V., Aral, A., & Brandic, I. (2022). Edge offloading for microservice architectures. In Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking (pp. 1–6). Association for Computing Machinery. https://doi.org/10.1145/3517206.3526266
    Download: Artikel (857 KB)
    Project: RUCON (2016–2023)
  • Increasing Traffic Safety with Real-Time Edge Analytics and 5G / Lujic, I., Maio, V. D., Pollhammer, K., Bodrozic, I., Lasic, J., & Brandic, I. (2021). Increasing Traffic Safety with Real-Time Edge Analytics and 5G. In Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking. 4th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2021), Edinburgh, United Kingdom of Great Britain and Northern Ireland (the). https://doi.org/10.1145/3434770.3459732
  • ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks / Aral, A., De Maio, V., & Brandic, I. (2021). ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks. IEEE Transactions on Sustainable Computing, 7(4), 761–773. https://doi.org/10.1109/tsusc.2021.3049850
  • SEA-LEAP: Self-adaptive and Locality-aware Edge Analytics Placement / Lujic, I., De Maio, V., Venugopal, S., & Brandic, I. (2021). SEA-LEAP: Self-adaptive and Locality-aware Edge Analytics Placement. IEEE Transactions on Services Computing, 15(2), 602–613. https://doi.org/10.1109/tsc.2021.3104458
  • A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers / Torre, E., Durillo, J. J., De Maio, V., Agrawal, P., Benedict, S., Saurabh, N., & Prodan, R. (2020). A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers. Information and Software Technology, 128(106390), 106390. https://doi.org/10.1016/j.infsof.2020.106390
  • Simulators and emulators for edge computing / Aral, A., & De Maio, V. (2020). Simulators and emulators for edge computing. In Edge Computing: Models, technologies and applications (pp. 291–311). IET The Institution of Engineering and Technology. https://doi.org/10.1049/pbpc033e_ch14
  • Resilient Edge Data Management Framework / Lujic, I., De Maio, V., & Brandic, I. (2020). Resilient Edge Data Management Framework. IEEE Transactions on Services Computing, 13(4), 663–674. https://doi.org/10.1109/tsc.2019.2962016
  • Multi-objective scheduling of extreme data scientific workflows in Fog / De Maio, V., & Kimovski, D. (2020). Multi-objective scheduling of extreme data scientific workflows in Fog. Future Generation Computer Systems: The International Journal of EScience, 106, 171–184. https://doi.org/10.1016/j.future.2019.12.054
  • Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big Data Framework: Principles, Practice, and Experiences / Cuzzocrea, A., De Maio, V., & Fadda, E. (2020). Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big Data Framework: Principles, Practice, and Experiences. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE. https://doi.org/10.1109/compsac48688.2020.00-69
  • Energy and Profit-Aware Proof-of-Stake Offloading in Blockchain-based VANETs / De Maio, V., Brundo Uriarte, R., & Brandic, I. (2019). Energy and Profit-Aware Proof-of-Stake Offloading in Blockchain-based VANETs. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing. UCC ’19: IEEE/ACM 12th International Conference on Utility and Cloud Computing, Auckland, New Zealand. https://doi.org/10.1145/3344341.3368797
  • Cloud-Based Federated Learning For Environmental Data / Aral, A., De Maio, V., & Brandic, I. (2019). Cloud-Based Federated Learning For Environmental Data. CHIST-ERA Conference 2019, Tallinn, Estonia. http://hdl.handle.net/20.500.12708/87028
  • First-Hop Mobile Offloading of DAG Computations / De Maio, V., & Brandic, I. (2018). First-Hop Mobile Offloading of DAG Computations. In 18th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid 2018) (pp. 1–10). http://hdl.handle.net/20.500.12708/57367
  • Multi-Objective Mobile Edge Provisioning in Small Cell Clouds / De Maio, V., & Brandic, I. (2018). Multi-Objective Mobile Edge Provisioning in Small Cell Clouds. In Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering. 10th ACM/SPEC International Conference on Performance Engineering (ICPE 2019), Mumbai, India, Non-EU. https://doi.org/10.1145/3297663.3310301
  • Adaptive Recovery of Incomplete Datasets for Edge Analytics / Lujic, I., De Maio, V., & Brandic, I. (2018). Adaptive Recovery of Incomplete Datasets for Edge Analytics. In 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC). 2nd IEEE International Conference on Fog and Edge Computing (ICFEC 2018), Washington DC, USA, Non-EU. IEEE. https://doi.org/10.1109/cfec.2018.8358726
  • Efficient Edge Storage Management Based on Near Real-Time Forecasts / Lujic, I., De Maio, V., & Brandic, I. (2017). Efficient Edge Storage Management Based on Near Real-Time Forecasts. In 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE. https://doi.org/10.1109/icfec.2017.9