Publications
Applied AI
Journals
- Bentivoglio, R., Isufi, E., Jonkman, S.N. and Taormina, R., 2023. Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks (Preprint). EGUsphere, 2023, pp.1-24.
- do Lago, C.A., Giacomoni, M.H., Bentivoglio, R., Taormina, R., Junior, M.N.G. and Mendiondo, E.M., 2023. Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks. Journal of Hydrology, 618, p.129276.
- Vrachimis, S. G., Eliades, D. G., Taormina, R., Kapelan, Z., Ostfeld, A., Liu, S., ... & Polycarpou, M. M. (2022). Battle of the leakage detection and isolation methods. Journal of Water Resources Planning and Management, 148(12), 04022068.
- Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.(2022, August). Deep learning methods for flood mapping: a review of existing applications and future research directions, Hydrol. Earth Syst. Sci., 26, 4345â4378.
- GarzĂłn, A., Kapelan, Z., Langeveld, J. and Taormina, R., (2022). Machine learningâbased surrogate modelling for Urban Water Networks: Review and future research directions. Water Resources Research, p.e2021WR031808.
- Di Nardo, A., Boccelli, D. L., Herrera, M., Creaco, E., Cominola, A., Sitzenfrei, R., & Taormina, R. (2021). Smart Urban Water Networks: Solutions, Trends and Challenges. Water 2021, 13, 501.
Conference Abstracts & Proceedings
- Bentivoglio, R., Isufi, E., Jonkman, S.N. and Taormina, R., 2023. On the generalization of hydraulic-inspired graph neural networks for spatio-temporal flood simulations (No. EGU23-12952). Copernicus Meetings.
- R. Bentivoglio, B. Kerimov, J. A. G. Diaz, E. Isufi, F. Tscheikner-Grati, D. B. Steffelbauer, R. Taormina, Assessing the Performance and Transferability of Graph Neural Network Metamodels for Water Distribution Systems, WSDA / CCWI Joint Conference, 2022.
- B. Kerimov, F. Tscheikner-Gratl, R. Taormina, D. Steffelbauer (2022) The Shape of Water Distribution Networks - Describing local structures of water networks via graphlet analysis. 2nd WDSA/CCWI Joint Conference Water Distribution System Analysis Computing and Control in Water Industry . University of Valencia; Valencia. 2022-07-18 - 2022-07-22.
- GarzĂłn, A., Bentivoglio, R., Isufi, E., Kapelan, Z., & Taormina, R. (2021, April). Modeling Water Distribution Systems with Graph Neural Networks. In EGU General Assembly Conference Abstracts (pp. EGU21-9378).
- van der Kooij, E., Schleiss, M., Taormina, R., Fioranelli, F., Lugt, D., van Hoek, M., ... & Overeem, A. (2021, April). Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach. In EGU General Assembly Conference Abstracts (pp. EGU21-12814).
- Mavritsakis, P., ten Veldhuis, M. C., Schleiss, M., & Taormina, R. (2021, April). Dry-spell assessment through rainfall downscaling comparing deep-learning algorithms and conventional statistical frameworks in a data scarce region: The case of Northern Ghana. In EGU General Assembly Conference Abstracts (pp. EGU21-8393).
- Taormina, R., & Isufi, E. (2020, December). Geometric Deep Learning for Modeling, Prediction and Forecasting in Urban Water Systems. In AGU Fall Meeting Abstracts (Vol. 2020, pp. H188-04).
- Taormina, R., Ashrafi, M., Murillo, A., & Galelli, S. (2020, May). Deep Learning-based Surrogate Models for Water Distribution Systems. In EGU General Assembly Conference Abstracts (p. 22576).
Fundamental AI
Journals
- Gao, Z., & Isufi, E. (2023). Learning stochastic graph neural networks with constrained variance. IEEE Transactions on Signal Processing, 71, 358-371.
- Money, R., Krishnan, J., Beferull-Lozano, B., & Isufi, E. (2023). Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models. IEEE Open Journal of Signal Processing, 4, 61-70.
- Money, R., Krishnan, J., Beferull-Lozano, B., & Isufi, E. (2022). Online Edge Flow Imputation on Networks. IEEE Signal Processing Letters.
- M. Yang, E. Isufi, M. T. Schaub and G. Leus, "Simplicial Convolutional Filters," in IEEE Transactions on Signal Processing, vol. 70, pp. 4633-4648, 2022, doi: 10.1109/TSP.2022.3207045.
- B. Das, A. Hanjalic and E. Isufi, Task-Aware Connectivity Learning for Incoming Nodes on Growing Graphs, in the IEEE Transactions on Signal and Information Processing over Networks, Aug. 2022.
- Z. Gao, E. Isufi and A. Ribeiro, Stability of Graph Convolutional Neural Networks to Stochastic Perturbations, Elsevier Signal Processing, Special Issue on Processing and Learning over Networks, Jun. 2021
- Z. Gao, E. Isufi and A. Ribeiro, Stochastic Graph Neural Networks, in the IEEE Transactions on Signal Processing, 2021.
- Gama, F., Isufi, E., Leus, G., & Ribeiro, A. (2020). Graphs, convolutions, and neural networks: From graph filters to graph neural networks. IEEE Signal Processing Magazine, 37(6), 128-138.
- Isufi, E., Gama, F., & Ribeiro, A. (2020). EdgeNets: Edge varying graph neural networks. arXiv preprint arXiv:2001.07620.
- Coutino, M., Isufi, E., & Leus, G. (2019). Advances in distributed graph filtering. IEEE Transactions on Signal Processing, 67(9), 2320-2333.
- Isufi, Elvin, et al. Autoregressive moving average graph filtering. IEEE Transactions on Signal Processing 65.2 (2016): 274-288.
- Isufi, E., Loukas, A., Simonetto, A., & Leus, G. (2017). Filtering random graph processes over random time-varying graphs. IEEE Transactions on Signal Processing, 65(16),
- Isufi, E., Loukas, A., Perraudin, N., & Leus, G. (2019). Forecasting time series with varma recursions on graphs. IEEE Transactions on Signal Processing, 67(18), 4870-4885.4406-4421.
Conference Abstracts & Proceedings
- Krishnan, J., Money, R., Beferull-Lozano, B., & Isufi, E. (2023, June). Simplicial Vector Autoregressive Model For Streaming Edge Flows. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE
- Z. Gao and E. Isufi, Learning Stable Graph Neural Networks via Spectral Regularization, IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA, Nov. 2022. (invited paper)
- M. Yang and E. Isufi, Simplicial Trend Filtering, IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA, Nov. 2022. (invited paper)
- B. Das and E. Isufi, Online Filtering over Expanding Graphs, IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA, Nov. 2022.
- Y. He, M. Coutino, E. Isufi, and G. Leus, Dynamic Bi-colored Graph Partitioning, EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, Aug. 2022. (invited paper)
- B. Das and E. Isufi, Graph Filtering Over Expanding Graphs, IEEE Data Science and Learning Workshop (DSLW), Singapore, May. 2022.
- E. Isufi and M. Yang, Convolutional Filtering in Simplicial Complexes, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 5578-5582.
- M. Yang, E. Isufi and G. Leus, Simplicial Convolutional Neural Networks, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8847-8851.
- B. Das and E. Isufi, Learning Expanding Graphs for Signal Interpolation, IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Singapore, May. 2022.
- M. Yang, E. Isufi, M. T. Schaub and G. Leus, Finite Impulse Response Filters for Simplicial Complexes, 2021 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 2005-2009.
- Isufi, E., & Mazzola, G. (2021). Graph-Time Convolutional Neural Networks. Data Science and Learning Workshop, 2021 (paper audience award)
- Ruiz, L., Gama, F., Ribeiro, A., & Isufi, E. (2021, June). Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5265-5269). IEEE.
- Leus, G., Yang, M., Coutino, M., & Isufi, E. (2021, June). Topological Volterra Filters. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5385-5399). IEEE.
- Gao, Z., Isufi, E., & Ribeiro, A. (2021, June). Variance-Constrained Learning for Stochastic Graph Neural Networks. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5245-5249). IEEE.
- Iancu, B., Ruiz, L., Ribeiro, A., & Isufi, E. (2020, September). Graph-Adaptive Activation Functions for Graph Neural Networks. 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
- Natali, A., Isufi, E., & Leus, G. (2020, May). Forecasting multi-dimensional processes over graphs. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5575-5579). IEEE.