Applied AI

Journals

  • 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, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022.
  • Garzón, A., Kapelan, Z., Langeveld, J. and Taormina, R., (2022). Machine learning‐based 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

  • Garzón, 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

  • 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.
  • Gao, Z., Isufi, E., & Ribeiro, A. (2021). Stability of Graph Convolutional Neural Networks to Stochastic Perturbations. Signal Processing, 108216.
  • Gao, Z., Isufi, E., & Ribeiro, A. R. (2021). Stochastic graph neural networks. IEEE Transactions on Signal Processing.
  • 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), 4406-4421.
  • 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.

Conference Abstracts & Proceedings

  • Yang, M., Isufi, E., Schaub, M. T., & Leus, G. (2021). Finite Impulse Response Filters for Simplicial Complexes. EUSIPCO, 2021
  • 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. In 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. In 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. In 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. In 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. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5575-5579). IEEE.
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