Publications
Conferences
- Money, R., Krishnan, J., Beferull-Lozano, B. and Isufi, E. (2024). Evolution Backcasting of Edge Flows from Partial Observations Using Simplicial Vector Autoregressive Models. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea.
- Möllers, A., Immer, A., Fortuin, V. and Isufi, E. (2024). Hodge-Aware Contrastive Learning. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. [https://arxiv.org/abs/2309.07364]
- Buciulea, A., Isufi, E., Leus, G. and Marques, A.G. (2024). Learning Graphs and Simplicial Complexes from Data. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea.
- Habib, B., Isufi, E. and Cremer, J.L. (2024). Weakly Supervised Graph Neural Network For State Estimation in Unobservable Distribution Systems. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea.
- Sabbaqi, M. and Isufi, E. (2024). Inferring Time Varying Signals over Uncertain Graphs. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea.
- Das, B. and Isufi, E. (2024). Tensor Graph Decomposition for Temporal Networks. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea.
- Yang, M. and Isufi, E. (2023). Convolutional Learning on Simplicial Complexes. International Conference on Machine Learning. [https://arxiv.org/pdf/2301.11163.pdf]
- Möllers, A., Immer, A., Isufi, E. and Fortuin, V. (2023). Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. 5th Symposium on Advances in Approximate Bayesian Inference collocated with ICML. [https://openreview.net/forum?id=LH76pl-OUj]
- Sabbaqi, M. and Isufi, E., (2023). Graph-Time Trend Filtering and Unrolling Network. EURASIP European Signal Processing Conference (EUSIPCO), Helsinki, Finland. [https://ieeexplore.ieee.org/document/10289885]
- Bentivoglio, R., Isufi, E., S. Jonkman, N. and Taormina, R. (2023). On the Generalization of Hydraulic-Inspired Graph Neural Networks for Spatio-temporal Flood Simulations. European Geoscience Union (EGU) General Assembly. [https://meetingorganizer.copernicus.org/EGU23/session/46382]
- Krishnan, J., Money, R., Beferull-Lozano, B. and Isufi, E. (2023). Simplicial Vector Autoregressive Model for Streaming Edge Flows. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/document/10289885]
- Das, B. and Isufi, E. (2023). Online Vector Autoregressive Models over Expanding Graphs. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/abstract/document/10096508]
- Yang, M., Das, B. and E.Isufi (2023). Online Edge Flow Prediction over Expanding Simplicial Complexes. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/abstract/document/10096364]
- Bentivoglio, R., Isufi, E., Nicolaas, S. and Taormina, R. (2023). Graph Neural Networks for Dike Breach Flood Mapping. 9th International Conference on Flood Management (ICFM9).
- Möllers, A., Immer, A., Isufi, E. and Fortuin, V. (2023). Dynamic Bi-colored Graph Partitioning. Fifth Symposium on Advances in Approximate Bayesian Inference. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:D03iK_w7-QYC]
- Krishnan, J., Money. R., Beferull-Lozano, B. and Isufi, E. (2023). Assessing the Performance and Transferability of Graph Neural Network Metamodels for Water Distribution Systems. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:a0OBvERweLwC]
- Yang, M., Das, B. and Isufi, E. (2023). The Shape of Water Distribution Networks - Describing local structures of water networks via graphlet analysis. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:yD5IFk8b50cC]
- Das, B. and Isufi, E. (2023). Graph Filtering Over Expanding Graphs. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:cFHS6HbyZ2cC]
- He, Y., Coutino, M., Isufi, E. and Leus, G. (2022). Convolutional Filtering in Simplicial Complexes. EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia. [https://research.tudelft.nl/en/publications/dynamic-bi-colored-graph-partitioning]
- Bentivoglio, R., Kerimov, B., Diaz, J.A.G., Isufi, E., Tscheikner-Grati, F., Steffelbauer, D.B. and Taormina, R. (2022). Simplicial Convolutional Neural Networks. 2nd WDSA/CCWI Joint Conference Water Distribution System Analysis Computing and Control in Water Industry; Valencia, Spain. [https://iwaponline.com/jh/article/doi/10.2166/hydro.2023.031/98159/Assessing-the-performances-and-transferability-of]
- Kerimov, B., Tscheikner-Gratl, F., Taormina, R. and Steffelbauer D. (2022). Learning Expanding Graphs for Signal Interpolation. 2nd WDSA/CCWI Joint Conference Water Distribution System Analysis Computing and Control in Water Industry; Valencia, Spain. [https://doi.org/10.4995/WDSA-CCWI2022.2022.14784]
- Das B. and Isufi, E. (2022). Learning Stable Graph Neural Networks via Spectral Regularization. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.08058]
- Isufi, E. and Yang. M. (2022). Simplicial Trend Filtering. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2201.12584v1]
- Yang, M., Isufi, E. and Leus, G. (2022). Online Filtering over Expanding Graphs. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2110.02585]
- Das, B. and Isufi, E. (2022). EPANET Metamodels with Deep Unrolling of the Global Gradient Algorithm. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2203.07966]
- Gao, Z. and Isufi, E. (2022). Graph-Time Convolutional Autoencoders. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://arxiv.org/abs/2211.06966#:~:text=Learning%20Stable%20Graph%20Neural%20Networks%20via%20Spectral%20Regularization,provides%20guarantees%20for%20architecture%20performance%20in%20noisy%20scenarios.]
- Yang, M. and Isufi, E. (2022). Convolutional Filters for Simplicial Complexes. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/10051892/authors#authors]
- Das, B. and Isufi, E. (2022). Modeling Water Distribution Systems with Graph Neural Networks. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://arxiv.org/abs/2301.06898v1]
- Roca, A.S., A. G. Díaz, Isufi, E. and Taormina, R. (2022). Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach. WSDA / CCWI Joint Conference.
- Sabbaqi, M., Taormina, R., Hanjalic, A. and Isufi, E. (2022). Dry-spell assessment through rainfall downscaling comparing deep-learning algorithms and conventional statistical frameworks in a data scarce region: The case of Northern Ghana. Proceedings of the First Learning on Graphs Conference (Log 2022), PMLR 198, Virtual. [https://openreview.net/forum?id=2HqKwHaBwv]
- Gao, Z. and Isufi, E. (2022). Finite Impulse Response Filters for Simplicial Complexes. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://arxiv.org/abs/2211.06966]
- Yang, M. and Isufi, E. (2022). Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/10051892]
- Das, B. and Isufi, E. (2022). Topological Volterra Filters. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/10052045]
- Bentivoglio, R., Kerimov, B., Diaz, , G., Isufi, E., Tscheikner-Grati, F., Steffelbauer, D.B. and Taormina, R. (2022). Variance-Constrained Learning for Stochastic Graph Neural Networks. WSDA / CCWI Joint Conference. [https://iwaponline.com/jh/article/doi/10.2166/hydro.2023.031/98159/Assessing-the-performances-and-transferability-of]
- He, Y., Coutino, M., Isufi, E., and Leus, G. (2022). Online Graph Learning from Time-Varying Structural Equation Models. EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia. [https://ieeexplore.ieee.org/document/9909839]
- Das, B. and Isufi, E. (2022). Sampling Graph Signal with Sparse Dictionary Representation. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.08058]
- Isufi, E. and Yang, M. (2022). Graph-Time Convolutional Neural Networks. EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia. [https://arxiv.org/abs/2201.12584]
- Yang, M., Isufi, E. and Leus, G. (2022). Online Time-Varying Topology Identification via Prediction-Correction Algorithms. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2110.02585]
- Das, B. and Isufi, E. (2022). GReS: Workshop on graph neural networks for recommendation and search. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.07966]
- Sabbaqi, M., Taormina, R., Hanjalic, A., Isufi, E. (2022). Sampling Graph Signals with Sparse Dictionary Representation. Learning on Graphs Conference. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:HoB7MX3m0LUC]
- Gao, Z., Isufi, E. (2022). Node varying regularization for graph signals. 56th Asilomar Conference on Signals, Systems, and Computers. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:M05iB0D1s5AC]
- Yang, M., Isufi, E. (2022). Towards finite-time consensus with graph convolutional neural networks. 56th Asilomar Conference on Signals, Systems, and Computers. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:fPk4N6BV_jEC]
- Das, B. and Isufi, E. (2022). State-space based network topology identification. 56th Asilomar Conference on Signals, Systems, and Computers. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:rO6llkc54NcC]
- He, Y., Coutino, M., Isufi, E., Leus, G. (2022). Geometric Deep Learning for Modeling, Prediction and Forecasting in Urban Water Systems. 30th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:70eg2SAEIzsC]
- Das, B., Isufi, E. (2022). Deep Learning-based Surrogate Models for Water Distribution Systems. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:J_g5lzvAfSwC]
- Isufi, E., Yang, M. (2022). Forecasting multi-dimensional processes over graphs. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:BqipwSGYUEgC]
- Yang, M., Isufi, E., Leus, G. (2022). Graph-Adaptive Activation Functions for Graph Neural Networks. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:JV2RwH3_ST0C]
- Garzón, A., Bentivoglio, R., Isufi, E., Kapelan, Z. and Taormina, R. (2021). Rational Chebyshev Graph Filters. EGU General Assembly Conference Abstracts. [https://meetingorganizer.copernicus.org/EGU21/EGU21-9378.html#:~:text=In%20this%20work%2C%20we%20introduce%20Graph%20Neural%20Networks,interpretations%20for%20using%20this%20framework%20in%20water%20networks.]
- van der Kooij, E., Schleiss, M., Taormina, R., Fioranelli, F., Lugt, D., Van Hoek, M., Leijnse, H. and Overeem, A. (2021). Active Semi-supervised Learning for Diffusions on Graphs. EGU General Assembly Conference Abstracts. [https://ui.adsabs.harvard.edu/abs/2021EGUGA..2312814V/abstract#:~:text=Nowcasting%20heavy%20precipitation%20over%20the%20Netherlands%20using%20a,extreme%20weather%20and%20its%20consequences%2C%20e.g.%20urban%20flooding.]
- Mavritsakis, P., Ten Veldhuis, M.C., Schleiss, M. and Taormina, R. (2021). Stochastic Graph Neural Networks. EGU General Assembly Conference Abstracts. [https://meetingorganizer.copernicus.org/EGU21/EGU21-8393.html]
- Yang, M., Isufi, E., Schaub, T. and Leus, G. (2021). Forecasting Multi-dimensional Graph Processes over Graphs. 29th European Signal Processing Conference (EUSIPCO), Dublin Ireland. [https://arxiv.org/abs/2103.12587]
- Ruiz, L., Gama, F., Ribeiro, A. and Isufi, E. (2021). Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada. [https://arxiv.org/abs/2010.14585]
- Leus, G., Yang, M., Coutino, M., & Isufi, E. (2021). Rapid Spatio-Temporal Flood Modeling via Hydraulics-Based Graph Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada. [https://research.tudelft.nl/en/publications/topological-volterra-filters]
- Gao, Z., Isufi, E.and Ribeiro, A. (2021). Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada. [https://arxiv.org/abs/2201.12611]
- Natali, A., Isufi, E., Coutino, M. and Leus, G. (2021). Deep Statistical Solver for Distribution System State Estimation. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/9723163]
- Yang, M., Isufi, E., Schaub, M., T. and Leus, G. (2021). Learning Stochastic Graph Neural Networks with Constrained Variance. EURASIP European Signal Processing Conference (EUSIPCO), Dublin, Ireland. [https://arxiv.org/abs/2103.12587]
- Zhang, K., Coutino, M. and Isufi, E. (2021). Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models. EURASIP European Signal Processing Conference (EUSIPCO), Dublin, Ireland. [https://ieeexplore.ieee.org/document/9615918]
- Isufi, E. and Mazzola, G. (2021). Assessing the performances and transferability of graph neural network metamodels for water distribution systems. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://ieeexplore.ieee.org/document/9523412]
- Garzon, A., Bentivoglio, R., Isufi, E., Kapelan, Z. and Taormina, R. (2021). Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks. European Geoscience Union (EGU) General Assembly. [https://ui.adsabs.harvard.edu/abs/2021EGUGA..23.9378G/abstract]
- Ruiz, L., Gama, F., Ribeiro, A. and Isufi, E. (2021). Deep learning methods for flood mapping: a review of existing applications and future research directions. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://arxiv.org/abs/2010.14585]
- Leus, G., Yang, M., Coutino, M. and Isufi, E. (2021). Task-Aware Connectivity Learning for Incoming Nodes on Growing Graphs. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://ieeexplore.ieee.org/abstract/document/9414275?casa_token=157zVouIS8MAAAAA:yXuxCkwxQlos0c3crfLGreUccNu2O_NVOOn2c7xnrt-eOqmn653fMfXW183K-VlbnVnLWE90uCY]
- Natali, A., Coutino, M., Isufi, E. and Leus, G. (2021). Battle of the leakage detection and isolation methods. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://ieeexplore.ieee.org/abstract/document/9415053?casa_token=-c75EUSohicAAAAA:1vlTIlLjdlZcM9IDIyskLc1El9Xf2tRmqtTBLaHBUccO0mcg0GnxxtsaPXUk5qn10ogjTgj3lDI]
- Gao, Z., Isufi, E. and Ribeiro, A. (2021). Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://ieeexplore.ieee.org/abstract/document/9413751?casa_token=WVchpmIibNQAAAAA:4MoiI1fbOMn3iVPpB0nqp_tSoOkRgDy0SGqbvi-9p9CJspQ_x8D7A_pvY98_lDPfRDw6OoYhjBE]
- Natali, A., Isufi, E., Coutino, M., Leus, G. (2021). Simplicial Convolutional Filters. 55th Asilomar Conference on Signals, Systems, and Computers. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:ns9cj8rnVeAC]
- Thonet, T., Clinchant, S., Lassance, C., Isufi, E., Ma, J., Xie, Y., Renders, J.-M. and Bronstein, M. (2021). Graph Filters for Signal Processing and Machine Learning on Graphs. 15th ACM Conference on Recommender Systems. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:M3NEmzRMIkIC]
- Zhang, K., Coutino, M., Isufi, E. (2021). Online Missing Data Imputation of Edge Flows. 29th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:hMod-77fHWUC]
- Yang, M., Isufi, E., Schaub, M.T., Leus, G. (2021). Learning Time-Varying Graphs from Online Data. 29th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:bEWYMUwI8FkC]
- Leus, G., Yang, M., Coutino, M., Isufi, E. (2021). Online edge flow imputation on networks. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:isC4tDSrTZIC]
- Gao, Z., Isufi, E., Ribeiro, A. (2021). Task-aware connectivity learning for incoming nodes over growing graphs. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:TFP_iSt0sucC]
- Ruiz, L., Gama, F., Ribeiro, A., Isufi, E. (2021). Papers not appearing in the proceedings. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:e5wmG9Sq2KIC]
- Natali, A., Coutino, M., Isufi, E., Leus, G. (2021). Smart Urban Water Networks: Solutions, Trends and Challenges. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:TQgYirikUcIC]
- Garzón, A., Bentivoglio, R., Isufi, E., Kapelan, Z., Taormina, R. (2021). Stability of Graph Convolutional Neural Networks to Stochastic Perturbations. EGU General Assembly Conference Abstracts. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:iH-uZ7U-co4C]
- Yang, M., Coutino, M., Isufi, E., Leus, G. (2021). Graphs, convolutionsand neural networks: From graph filters to graph neural networks. 28th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:HDshCWvjkbEC]
- Iancu, B., Isufi, E. (2021). Quantization Analysis and Robust Design for Distributed Graph Filters. 28th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:mB3voiENLucC]
- Coutino, M., Isufi, E., Maehara, T., Leus, G. (2021). EdgeNets: Edge Varying Graph Neural Networks. 28th European Signal Processing Conference (EUSIPCO). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wvywFdwAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=wvywFdwAAAAJ:_kc_bZDykSQC]
- Taormina, R., & Isufi, E. (2020). Node-Adaptive Regularization for Graph Signal Reconstruction. AGU Fall Meeting Abstracts. [https://ui.adsabs.harvard.edu/abs/2020AGUFMH188...04T/abstract]
- Taormina, R., Ashrafi, M., Murillo, A. and Galelli, S. (2020). Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions. Deep Learning-based Surrogate Models for Water Distribution Systems - NASA/ADS (harvard.edu). [https://ui.adsabs.harvard.edu/abs/2020EGUGA..2222576T/abstract]
- Natali, A., Isufi, E. and Leus, G. (2020). Graphs, Convolutions and Neural Networks: From Graph Filters to Graph Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona Spain. [https://ieeexplore.ieee.org/document/9053522]
- Iancu, B., Ruiz, L., Ribeiro, A. and Isufi, E. (2020). Observing and Tracking Bandlimited Graph Processes. IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), Espoo, Finland. [https://arxiv.org/abs/2009.06723]
- Taormina, R. and Isufi, E. (2020). Graph-Time Spectral Analysis for Atrial Fibrillation. Advancing Earth and Space Science (AGU) Fall Meeting. [https://ui.adsabs.harvard.edu/abs/2020AGUFMH188...04T/abstract]
- Rimleanscaia, O. and Isufi, E. (2020). State-Space Network Topology Identification from Partial Observations. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [Rational Chebyshev Graph Filters | IEEE Conference Publication | IEEE Xplore]
- Iancu, B., Ruiz, L., Ribeiro, A. and Isufi, E. (2020). Observing and tracking bandlimited graph processes from sampled measurements. IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Finland. [https://ieeexplore.ieee.org/document/9231732]
- Iancu, B. and Isufi, E. (2020). Graphs, convolutions, and neural networks: From graph filters to graph neural networks. EURASIP European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands. [https://ieeexplore.ieee.org/abstract/document/9287610]
- Yang, M., Coutino, M., Isufi, E. and Leus, G. (2020). Graph-Time Convolutional Neural Networks. EURASIP European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands. [https://ieeexplore.ieee.org/abstract/document/9287807?casa_token=mluzRXdVFKQAAAAA:MYSCeCSGm9M5PFwfT15-qbK9Rd0PkOXGnDaR50rP8e6MeDm07coNzZrInpOAsLDy-plEQKFgd3M]
Journals
[14] | Bastien Giraud, Ali Rajaei, Jochen L. Cremer “Constraint-Driven Deep Learning for N-k Security Constrained Optimal Power Flow”, Electric Power System Research and 2024 IEEE Power System Computation Conference |
[13] | Nikolina Covic, Jochen L. Cremer, Hrvoje Pandžić, “Learning a Reward Function for Optimal Appliance Scheduling” Electric Power System Research and 2024 IEEE Power System Computation Conference arxiv.org/pdf/2310.07389.pdf |
[12] | Charles Renshaw-Whitman, Viktor Zobernig, Jochen L. Cremer, Laurens de Vries, ”The Non-Stationary for Multiagent Reinforcement Learning in Electricity Markets”, Electric Power System Research and 2024 IEEE Power System Computation Conference |
[11] | Al-Amin Bugaje, Jochen L. Cremer, Goran Strbac ”Generating Quality Datasets for Real-Time Security Assessment: Balancing Historically Relevant and Rare Feasible Operating Condition” International Journal of Electrical Power & Energy Systems, 2023 |
[10] | B. Habib, E. Isufi, W. v. Breda, A. Jongepier and Jochen L. Cremer, ”Deep Statistical Solver for Distribution System State Estimation, ” IEEE Transactions on Power Systems, 2023, doi: 10.1109/TPWRS.2023.3290358. |
[9] | Dariush Wahdany, Carlo Schmitt, Jochen L. Cremer, ”More than Accuracy: End-To-End Wind Power Forecasting that Optimises the Energy System”, Electric Power System Research, 2023 |
[8] | Nidarshan Veera Kumar, Jochen L. Cremer, Marjan Popov, ”Incremental learning for real-time electrical disturbance event recognition”, International Journal of Electrical Power & Energy Systems, 2023, (108988) |
[7] | Al-Amin Bugaje, Jochen L. Cremer, Goran Strbac, “Real-time Transmission Switching with Neural Networks” IET Generation, Transmission & Distribution, 2022 |
[6] | Al-Amin Bugaje, Jochen L. Cremer, Goran Strbac, “Split-based Sequential Sampling for Realtime Security Assessment”, International Journal of Electrical Power & Energy Systems, 2022 |
[5] | Federica Bellizio, Jochen L. Cremer, Goran Strbac, ”Transient Stable Corrective Control in Smart Grids Using Neural Lyapunov Learning”, IEEE Transactions of Power Systems, 2022 |
[4] | Antoine Marot, Benjamin Donnot, Karim Chaouache, Adrian Kelly, Qiuhua Huang, Ramij-Raja Hossain, and Jochen L. Cremer. ”Learning to run a power network with trust.” arXiv preprint arXiv:2110.12908. Electric Power Systems Research, 2022 |
[3] | Antoine Marot, Adrian Kelly, Matija Naglic, Vincent Barbesant, Jochen Cremer, Alexandru Stefanov and Jan Viebahn, ”Perspectives for Future Power System Control Centers for The Energy Transition”, IEEE Journal of Modern Power Systems and Clean Energy, 2022 |
[2] | Federica Bellizio, Wangkun Zu, Dawei Qiu, Yujian Ye, Dimitrios Papadaskapoulos, Jochen L. Cremer, Fei Teng, Goran Strbac, “Transition to secure data-driven grid control and decentralized electricity market”, IEEE Proceedings, Special Issue "The Evolution of Smart Grids", 2022 |
[1] | Federica Bellizio, Al-Amin B. Bugaje, Jochen L. Cremer, Goran Strbac, “Verifying Machine Learning Conclusions for Securing Low Inertia Systems”, Sustainable Energy, Grids and Networks, 2022 |
Journals
[11] | Jingwei Dong, Arman Sharifi Kolarijani, and Peyman Mohajerin Esfahani, “Diagnosis for Switched Affine Systems with noisy Measurement”, Automatica, 2023 |
[10] | Pedro Zattoni Scroccaro, Arman Sharifi Kolarijani, and Peyman Mohajerin Esfahani, “Adaptive Online Optimization with Predictions: Static and Dynamic Environments”, IEEE Transactions on Automatic Control, 2023 |
[9] | V. A. Nguyen, S. Shafieezadeh-Abadeh, D. Kuhn, and P. Mohajerin Esfahani, “Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization”, Mathematics of Operations Research, 2022 |
[8] | M. Saeed Sarafraz, A. Proskurnikov, M. S. Tavazoei, and P. Mohajerin Esfahani, “Robust Output Regulation: Optimization-Based Synthesis and Event-Triggered Implementation”, IEEE Transactions on Automatic Control, 2022 |
[7] | C. van der Ploeg, E. Silvas, N. v. de Wouw, and P. Mohajerin Esfahani, “Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems”, IEEE Control Systems Letters, vol. 6, pp. 1988 - 1993, 2021 |
[6] | K. Pan, P. Palensky, and P. Mohajerin Esfahani, “Dynamic Anomaly Detection with High-fidelity Simulators: A Convex Optimization Approach”, IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1500-1515, 2022 |
[5] | S. A. Akhtar, A. S. Kolarijani and P. Mohajerin Esfahani, “Learning for Control: An Inverse Optimization Approach”, IEEE Control Systems Letters, vol. 6, pp. 187-192, 2021 |
[4] | B. Gravell, P. Mohajerin Esfahani, and T. Summers, “Learning Robust Controllers for Linear Quadratic Systems with Multiplicative Noise via Policy Gradient”, IEEE Transactions on Automatic Control, vol. 66, no. 11, pp. 5283-5298, 2021 |
[3] | B. V. Parys, P. Mohajerin Esfahani, and D. Kuhn, “From Data to Decisions: Distributionally Robust Optimization is Optimal”, Management Science, vol. 67, no. 6, pp. 3387-3402, 2021 |
[2] | A. Kolarijani, A. Proskurnikov, and P. Mohajerin Esfahani, “Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions” IEEE Transactions on Automatic Control, vol. 66, no. 3, pp. 1174-1189, 2021 |
[1] | V. Nguyen, D. Kuhn, and P. Mohajerin Esfahani, “Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator”, Operations Research (OR), vol. 70, no. 1, pp. 490-515, 2021 |
Conference Abstracts & Proceedings
[3] | A. Kolarijani, G. Max, and P. Mohajerin Esfahani, “Fast Approximate Dynamic Programming for Infinite-Horizon Continuous-State Markov Decision Processes”, Neural Information Processing Systems (NeurIPS), December 2021 |
[2] | R. Vreugdenhil, V. A. Nguyen, A. Eftekhari, P. Mohajerin Esfahani, “Principal Component Hierarchy for Sparse Quadratic Programs”, International Conference on Machine Learning (ICML), Vienna, Austria, July 2021 |
[1] | J. Dong, A. Sharifi Kolarijani, and P. Mohajerin Esfahani, “Multimode Diagnosis for Switched Affine Systems”, American Control Conference (ACC), New Orleans, USA, May 2021 |
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