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Dive into the research topics where Salah Ghamizi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Projects
- 1 Active
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FM2MRI: Foundation Multi-modal Model for Modality Synthesis & Segmentation of Magnetic Resonance Images
Ghamizi, S. (PI) & Keunen, O. (Scientific Advisor/Mentor)
FNR - Fonds National de la Recherche
5/01/25 → 5/10/27
Project: Research
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On the Impact of Industrial Delays when Mitigating Distribution Drifts: An Empirical Study on Real-World Financial Systems
Simonetto, T., Cordy, M., Ghamizi, S., Traon, Y. L., Lefebvre, C., Boystov, A. & Goujon, A., 2025, Discovering Drift Phenomena in Evolving Landscapes - 1st International Workshop, DELTA 2024, Proceedings. Piangerelli, M., Prenkaj, B., Rotalinti, Y., Joshi, A. & Stilo, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 57-73 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15013 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems
Ghamizi, S., Cao, J., Ma, A. & Rodriguez, P., Jan 2025, In: IEEE Transactions on Power Systems. 40, 1, p. 1148-1151 4 p.Research output: Contribution to journal › Article › Research › peer-review
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Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
Simonetto, T., Ghamizi, S. & Cordy, M., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
1 Citation (Scopus) -
OPF-HGNN: Generalizable Heterogeneous Graph Neural Networks for AC Optimal Power Flow
Ghamizi, S., Ma, A., Cao, J. & Rodriguez Cortes, P., 2024, 2024 IEEE Power and Energy Society General Meeting, PESGM 2024. IEEE Computer Society, (IEEE Power and Energy Society General Meeting).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
Simonetto, T., Ghamizi, S. & Cordy, M., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review