Alumni

M. Sc. students

1. Athanasios Georgoutsos (2025 cum laude) Lagged Spatiotemporal Covariance Neural Networks

2. Irtaza Hashmi (2024) PyTSPL: A Python Library for Topological Signal Processing and Learning

3. Radu Gaghi (2024) Multi-agent Reinforcement Learning for Radar Waveform Design

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

5. Rodrigo Revilla Llaca (2024) Wind Power Forecasting in Wind Farms using GNNs

6. Siert Sebus (2024) An Experimental Assessment of the Stability of Graph Contrastive Learning

7. Albert Solà Roca (2023) GGANET: Algorithm Unrolling for Water Distribution Networks Metamodeling

8. Alex Möllers (2023 cum laude) Bayesian Contrastive Learning on Topological Structures

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

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

11. Chengen Liu (2023) Simplicial Unrolling ElasticNet for Edge Flow Signal Reconstruction 

12. Rohan Chandrashekar (2022) Graph Regularized Tensor Decomposition for Recommender Systems 

13. Simon Dahrs (2022) Pure Cold Start Recommendation by Learning on Stochastically Expanded Graphs 

14. Benjamin Habib (2022 cum laude) Deep Statistical Solver for Distribution System State Estimation 

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

16. Gaia Zin (2021)  Investigation of focal epilepsy using graph signal processing 

17. Vasco de Bruijn (2021)  Side-Channel Analysis with Graph Neural Networks 

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

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

20. Maosheng Yang (2020 cum laude) Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction 

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

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

23. Bishwadeep Das (2019 cum laude) Active Semi-Supervised Learning For Diffusions on Graphs 

24. Ashvant Mahabir (2017) Blind Graph Topology Change Detection 

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

24. 1. R. Kalisvaart, M. Mansoury, A. Hanjalic and E. Isufi, Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation, ACM Transactions on Recommender Systems, Mar. 2025. [PDF]

    2. 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)

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

    4. 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.

    5. 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.

    6. 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

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

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

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

    10. 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.

    11. 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.

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

    13. 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)

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

    15. 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. Data Augmentation fo Bycycle ETA Estimation (2025)

Veena Madhu - Mels Lutgerink - Lucas Petre - Maroje Boršić

2. Collaborative Filtering via Covariance Neural Networks (2025)

Martin Angelov - Jort Boon - Ivan V. Bozhanin - Vic Vansteeland - Timothy Axel

3. Investigating Stability of Graph Neural Networks (2024)

Rauno Arike - Yigit Colakoglu - Khoa Nguyen - Vladimir Rullens - Alex Brown

4. Collaborative Filtering via Graph Regularizers (2022)

Melle Koper - Karolis Mariunas - Sérénic Monté - Lars van Blokland

5. A Deep Learning Study for Earthquake Detection (2022)

Kevin Zhu - Glenn van den Belt - Pijus Krisiukenas - Amaury Charlot 

Gancho Georgiev -- Irtaza Hashmi - Daniel van den Akker - Maikel Houbaer -  Xiangyu Du