My research in Computer Vision and Machine Learning revolves around the problems of generalization and robustness to changing environments and perturbations of the input data. I focus on identifying and mitigating the sources of bias in training data, while inducing good bias via expert and prior knowledge in the training process, model architectures or labels.
I address the understanding of the training data characteristics and the learning dynamics of computer vision models, to identify sources of bias and counteract them. I investigate the use of prior knowledge, in the form of improved labeling procedures to better exploit data semantics, in the design of novel architectural elements or to steer the learning process (e.g. via physics-informed models) for the training of data-efficient models, taking into account aspects related to training time and energy consumption. I envision efficient and robust computer vision models with embedded prior knowledge that are able to perform unbiased predictions, exploiting the semantics of the data rather than shortcut solutions, with reduced training time, size of the parameter space and energy consumption, with comparable performance to larger models trained with massive datasets and with higher computational requirements.
Ongoing PhD supervision
Shunxin Wang (University of Twente, Data Management and Biometrics group)
Zohra Rezgui (University of Twente, Data Management and Biometrics group)
Sven Dummer (University of Twente, Mathematics of Imaging and AI group)
Melissa Tijink (University of Twente, Data Management and Biometrics group)
Peter van der Wal (University of Groningen, Information systems group)
Completed PhD supervision
Dr. Maria Leyva Vallina (University of Groningen, The Netherlands - 2023)
Dr. Virginia Riego del Castillo (University of Leon, Spain - 2022)
Dr. Vincenzo Vigilante (University of Salerno, Italy - 2021)
Publications
Jump to: 2025 | 2024
Multilayer perceptron ensembles in a truly sparse training context (2025)Neural Computing and Applications (E-pub ahead of print/First online). van der Wal, P. R. D., Strisciuglio, N., Azzopardi, G. & Mocanu, D. C.https://doi.org/10.1007/s00521-025-11294-3Robust computer vision with applications to microscopic image analysis (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Wang, S.https://doi.org/10.3990/1.9789036564625Editorial: Computer vision and AI in real-world applications: robustness, generalization, and engineering (2025)Frontiers in Computer Science, 7. Article 1585443. Bruno, A., Mazzeo, P. L., Strisciuglio, N., Hammer, B. & Gao, M.https://doi.org/10.3389/fcomp.2025.1585443Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2025)In The Thirteenth International Conference on Learning Representations. Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., Keulen, M. v., Mocanu, D. C. & Mocanu, E.https://openreview.net/forum?id=daUQ7vmGapEnhanced demographic privacy in face recognition: From images to templates (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Rezgui, Z.https://doi.org/10.3990/1.9789036564489PushPull-Net: Inhibition-Driven ResNet Robust to Image Corruptions (2025)In Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part VIII (pp. 391-408). Article Chapter 26 ( Lecture Notes in Computer Science; Vol. 15308). Springer. Bennabhaktula, G. S., Alegre, E., Strisciuglio, N. & Azzopardi, G.https://doi.org/10.1007/978-3-031-78186-5_26 RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (2024)SIAM journal on imaging sciences, 17(4), 2302 - 2330. Dummer, S., Brune, C. & Strisciuglio, N.https://doi.org/10.1137/24M1644730Controllable Privacy in Face Recognition: A Filter-based Approach (2024)In 2024 IEEE International Joint Conference on Biometrics (IJCB). Article 10744519. IEEE. Rezgui, Z., Strisciuglio, N. & Veldhuis, R.https://doi.org/10.1109/IJCB62174.2024.10744519Synthetic Data-Based Training of Instance Segmentation: A Robotic Bin-Picking Pipeline for Chicken Fillets (2024)In 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024 (pp. 2805-2812) (IEEE International Conference on Automation Science and Engineering). IEEE. Jonker, M., Roozing, W. & Strisciuglio, N.https://doi.org/10.1109/CASE59546.2024.10711568Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2024)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., van Keulen, M., Mocanu, D. C. & Mocanu, E.https://doi.org/10.48550/arXiv.2410.03030CAST: Clustering self-Attention using Surrogate Tokens for efficient transformers (2024)Pattern recognition letters, 186, 30-36. van Engelenhoven, A., Strisciuglio, N. & Talavera, E.https://doi.org/10.1016/j.patrec.2024.08.024Fourier-Basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification (2024)In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17763-17772). Article 10655510. IEEE. Vaish, P., Wang, S. & Strisciuglio, N.https://doi.org/10.1109/CVPR52733.2024.01682PushPull-Net: Inhibition-Driven ResNet Robust to Image Corruptions (2024)[Working paper › Preprint]. ArXiv.org. Bennabhaktula, G. S., Alegre, E., Strisciuglio, N. & Azzopardi, G.https://doi.org/10.48550/arXiv.2408.04077Indoor scene recognition from images under visual corruptions (2024)[Working paper › Preprint]. de Lima Costa, W., Ismayilov, R., Strisciuglio, N. & Martinez, E. T.https://doi.org/10.48550/arXiv.2408.13029Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI (2024)In 2024 IEEE International Symposium on Biomedical Imaging (ISBI): 27-30 May, 2024 - Athens, Greece, Megaron Athens International Conference Centre (pp. 1-5). Article 10635502. IEEE. Laso, P., Cerri, S., Sorby-Adams, A., Guo, J., Mateen, F., Goebl, P., Wu, J., Liu, P., Li, H. B., Young, S. I., Billot, B., Puonti, O., Sze, G., Payabavash, S., DeHavenon, A., Sheth, K. N., Rosen, M. S., Kirsch, J., Strisciuglio, N., … Iglesias, J. E.https://doi.org/10.1109/ISBI56570.2024.10635502 Research profiles
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