Alumni

M. Sc. students

1. Radu Gaghi (2024) Multi-agent Reinforcement Learning for Radar Waveform Design. Together with Dr. Mario Coutino (TNO)

2. Alex Jeleniewski (2024) Online Adaptive Graph Neural Networks

3. Rodrigo Revilla Llaca (2024) Wind Power Forecasting in Wind Farms using GNNs. Together with Dr. Hadi Jamali-Rad.

4. Siert Sebus (2024) An Experimental Assessment of the Stability of Graph Contrastive Learning. Together with Dr. Hadi Jamali-Rad

5. Albert Solà Roca (2023) GGANET: Algorithm Unrolling for Water Distribution Networks Metamodeling. Together with Dr. Riccardo Taormina

6. Alex Möllers (2023) Bayesian Contrastive Learning on Topological Structures Together with Dr. Vincent Fortuin

7. Titus Naber (2023) Sparse & Interpretable Graph Attention Networks

8. Sneha Lodha (2023) From Clicks to Conscious Choices

9. Chengen Liu (2023) Simplicial Unrolling ElasticNet for Edge Flow Signal Reconstruction Together with Prof. Geert Leus

10. Rohan Chandrashekar (2022) Graph Regularized Tensor Decomposition for Recommender Systems Together with Dr. Kim Batselier

11. Simon Dahrs (2022) Pure Cold Start Recommendation by Learning on Stochastically Expanded Graphs Together with Ir. Bishwadeep Das.

12. Benjamin Habib (2022 cum laude) Deep Statistical Solver for Distribution System State Estimation Together with Dr. Jochen Cremer.

13. Raoul Kalisvaart (2022 cum laude) Nudging Towards Sustainable Choices via Recommender Systems

14. Gaia Zin (2021)  Investigation of focal epilepsy using graph signal processing Together with Dr. Borbála Hunyadi. 

15. Vasco de Bruijn (2021)  Side-Channel Analysis with Graph Neural Networks Together with Dr. Stjepan Picek. 

16. Matteo Pocchiari (2020)  Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions

17. Tomas Sipko (2020) Identifying Author Fingerprints in Texts via Graph Neural Networks.

18. Maosheng Yang (2020 cum laude) Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction Together with Dr. Mario Coutino and Prof. Geert Leus.

19. Bianca Iancu (2020 cum laude) Graph-Adaptive Activation Functions for Graph Neural Networks

20. Gabriele Mazzola (2020 cum laude) Graph-Time Convolutonal Neural Network: Learning from Time-Varying Signals Defined on Graph

21. Bishwadeep Das (2019 cum laude) Active Semi-Supervised Learning For Diffusions on Graphs Together with Prof. Geert Leus.

22. Ashvant Mahabir (2017) Blind Graph Topology Change Detection Together with Prof. Geert Leus.

Published theseses (and other publications with M.Sc. students)

22. 1. A. Möllers, A. Immer, V. Fortuin and E. Isufi, Hodge-Aware Contrastive Learning, IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), South Korea, Apr. 2024.  (invited paper)

    2. C. Liu, G. Leus and E. Isufi, Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction, IEEE Open Journal on Signal Processing, Dec. 2023. 

    3. A. Möllers, A. Immer, E. Isufi and V. Fortuin, Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks, 5th Symposium on Advances in Approximate Bayesian Inference, collocated with ICML, Jul. 2023.

    4. A. S. Roca, A. G. Díaz, E. Isufi and R. Taormina, EPANET Metamodels with Deep Unrolling of the Global Gradient Algorithm, WSDA / CCWI Joint Conference, 2023.

    5. B. Habib, E. Isufi, W. van Breda, A. Jongepier and J. L. Cremer, Deep Statistical Solver for Distribution System State Estimation, IEEE Transactions on Power Systems, Jun. 2023

    6. E. Isufi, M. Pocchiari and A. Hanjalic, Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions, Elsevier Information Processing and Management, Jul. 2020.

    7. M. Yang, M. Coutino, G. Leus and E. Isufi, Node-Adaptive Regularization for Graph Signal Reconstruction, IEEE Open Journal of Signal Processing, 2021.

    8. M. Yang, M. Coutino, E. Isufi and G. Leus, Node Varying Regularization for Graph Signals, EURASIP European Signal Processing Conference, Aug. 2020. (invited paper)

    9. B. Iancu, L. Ruiz, A. Ribeiro and E. Isufi, Graph-Adaptive Activation Functions For Graph Neural Networks, IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Finland, Sep. 2020.

    10. B. Iancu and E. Isufi, Towards Finite-Time Consensus with Graph Convolutional Neural Networks, EURASIP European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands, Aug. 2020.

    11. E. Isufi and G. Mazzola, Graph-Time Convolutional Neural Networks, IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada, Jun. 2021. 

    12. B. Das, E. Isufi and G. Leus, Active Semi-supervised Learning for Diffusions on Graphs, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May. 2020. (invited paper)

    13. B. Das, E. Isufi, G. Leus, Distributed Kernel-Based Reconstruction of Graph Signals, WIC Symposium 2019. (student travel grant award)

    14. E. Isufi, A. S. U. Mahabir and G. Leus, Blind Graph Topology Change Detection, IEEE Signal Processing Letters, vol. 15 (5), pp. 655 - 659,  2018. (presented also at ICASSP 2019)

B. Sc. students

 Research Projects @ TU Delft

1. Rauno Arike (2024) A Beyond Spectral Graph Theory: An Explainability-Driven Approach to Analysis the Stability of GNNs to Topology Perturbations

2. Yigit Colakoglu (2024) An Experimental Look at the Stability of GNNs Againts Topological Perturbations

3. Khoa Nguyen (2024) Investigation of Stability Property of GNN Architectures Under Domain Perturbation

4. Vladimir Rullens (2024) The Impact of GNN Task Types on the Stability of GNNs in Face of Perturbations

5. Alex Brown (2024) Stability of GNNs w.r.t. Different Types of Topological Perturbations

6. Melle Koper (2022) Item-Item Collaborative Filtering via Graph Regularization

7. Karolis Mariunas (2022) Performance of Total Variation Regularizer for User Collaborative Filtering

8. Sérénic Monté (2022) Tikhonov and Sobolev regularizers compared to user-based KNN collaborative filtering

9. Lars van Blokland (2022) Total Variation Regularization for Item KNN Collaborative Filtering: Performance Analysis

10. Kevin Zhu (2022) Predicting Micro-Earthquakes with Deep Neural Networks

11. Glenn van den Belt (2022) Short-term Earthquake Prediction with Deep Neural Networks

12. Pijus Krisiukenas(2022) Impact of Focal Depth on Short-Term Earthquake Prediction using Deep Learning

13. Amaury Charlot (2022) How long before strike can we predict earthquakes with an LSTM neural network?

14. Gancho Georgiev (2022)  Impact of seismic wavelength to detect high-magnitude earthquakes via deep learning

15. Irtaza Hashmi (2022) How does a CNN mixes with LSTM methods compare with the individual one in predicting earthquakes?

16. Daniel van den Akker (2022) Earthquake prediction: An MLP & SVM Comparison

17. Maikel Houbaer (2022) Comparing Multichannel Mixed CNN-RNN to individual models for earthquake prediction

18. Xiangyu Du (2022) Short-term Earthquake Prediction via Recurrent Neural Network Models