👋🏼 Hello there!
About Me 📜
I’m a Computer Engineering PhD student in the Institute for Intelligent Networked Systems at Northeastern University, Boston. I work as a Research Assistant in Mentis Laboratory, advised by Prof. Francesco Restuccia.
I hold a Master’s degree in Artificial Intelligence & Robotics, and a Bachelor’s degree in Computer & Control Engineering, both from Sapienza University of Rome. My Research interests are Interpretability and robustness in Machine Learning, with a focus in Geometric Deep Learning.
News 📰
- TAG-DS 2026 🏆 With Dario Loi as team howyadoin, I won first place in Track 1 of the Topological Deep Learning Challenge 2026 for our DSNN implementation in TopoBench, plus an honorable mention for our portfolio across both tracks. The challenge paper is out in PMLR.
- METACOG 2026 A Metacognitive Framework for Test-Time Adaptation received a Top 3 Paper Award at the AAAI Fall Symposium on Artificial Metacognition, where we also present a metacognitive perspective on anomaly detection.
- NeurIPS 2026 PLACE was accepted at NeurIPS 2026, and Heterogeneous Sheaf Neural Networks at the NeurReps workshop (Proceedings Track).
- IVC 2026 Caps-ViT is published in Image and Vision Computing.
Selected Publications 📄
PLACE: Patch-Level Agnostic Concept Extraction
Advances in Neural Information Processing Systems (NeurIPS 2026)
Label-free concept extraction on the patch tokens of frozen vision transformers, with a heatmap for every concept, about 20× faster than KL-supervised extraction.
@inproceedings{onorato2026place,
title = {{PLACE}: Patch-Level Agnostic Concept Extraction},
author = {Onorato, Gabriele and Silvestri, Fabrizio and Bastian, Nathaniel D. and Restuccia, Francesco},
booktitle = {Advances in Neural Information Processing Systems},
year = {2026},
url = {https://openreview.net/forum?id=8QlhKxCYSB}
}
Caps-ViT: A Synthetic-Free Capsule Vision Transformer for Reconstruction Based Anomaly Detection and Localization
Image and Vision Computing (Elsevier, 2026)
A capsule vision transformer for industrial anomaly detection, trained only on real normal images with no synthetic defects and no external pretraining.
@article{onorato2026capsvit,
title = {{Caps-ViT}: A Synthetic-Free Capsule Vision Transformer for Reconstruction Based Anomaly Detection and Localization},
author = {Onorato, Gabriele and Tarantelli, Kristjan and Guida, Alberto and Schiavella, Claudio and Amerini, Irene},
journal = {Image and Vision Computing},
volume = {176},
pages = {106204},
year = {2026},
publisher = {Elsevier},
doi = {10.1016/j.imavis.2026.106204}
}
Heterogeneous Sheaf Neural Networks
NeurIPS 2026 Workshop on Symmetry and Geometry in Neural Representations (NeurReps, PMLR)
Cellular sheaves for heterogeneous graphs: type-aware restriction maps replace relation-specific modules, with up to 10× fewer parameters.
@inproceedings{braithwaite2026hetero,
title = {Heterogeneous Sheaf Neural Networks},
author = {Braithwaite, Luke and Borgi, Alessio and Onorato, Gabriele and Tarantelli, Kristjan and Restuccia, Francesco and Silvestri, Fabrizio and Li{\`o}, Pietro},
booktitle = {NeurIPS 2026 Workshop on Symmetry and Geometry in Neural Representations (Proceedings Track)},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
year = {2026},
eprint = {2409.08036},
archivePrefix = {arXiv}
}
A Metacognitive Framework for Test-Time Adaptation: Taxonomy and Research Challenges
AAAI Fall Symposium on Artificial Metacognition (METACOG 2026) Top 3 Paper Award
Test-time adaptation as a two-level metacognitive system, with a taxonomy of existing protocols and the case for monitoring grounded in internal concepts.
@inproceedings{loi2026metacognitive,
title = {A Metacognitive Framework for Test-Time Adaptation: Taxonomy and Research Challenges},
author = {Loi, Dario and Onorato, Gabriele and Coviello, Antonella and Rifat, Shahriar and Bastian, Nathaniel D. and Restuccia, Francesco},
booktitle = {Proceedings of the AAAI Fall Symposium on Artificial Metacognition (METACOG)},
series = {AAAI Fall Symposium Series},
address = {Arlington, VA, USA},
month = {November},
year = {2026}
}
A Metacognitive Perspective on Anomaly Detection with Vision Foundation Models
AAAI Fall Symposium on Artificial Metacognition (METACOG 2026)
Monitoring and deciding dissociate in anomaly detection: Mahalanobis scoring monitors better, and an EVT threshold recovers its segmentation without anomalies.
@inproceedings{galiazzo2026metacognitive,
title = {A Metacognitive Perspective on Anomaly Detection with Vision Foundation Models},
author = {Galiazzo, Matteo and Onorato, Gabriele and Loi, Dario and Bastian, Nathaniel D. and Restuccia, Francesco},
booktitle = {Proceedings of the AAAI Fall Symposium on Artificial Metacognition (METACOG)},
series = {AAAI Fall Symposium Series},
address = {Arlington, VA, USA},
month = {November},
year = {2026}
}
Topological Deep Learning Challenge 2026: Bridging the Gap
Proceedings of the 2nd Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2026, PMLR) Track 1 Winner
The 2026 TDL Challenge set GNNs and TNNs side by side in TopoBench on GraphUniverse graphs; our DSNN implementation took first place in Track 1.
@inproceedings{pmlr-v334-bernardez26a,
title = {Topological Deep Learning Challenge 2026: Bridging the Gap},
author = {Bern{\'a}rdez, Guillermo and Telyatnikov, Lev and Papillon, Mathilde and Montagna, Marco and Cornelis, Louisa and Van Langendonck, Louis and Purificato, Antonio and Neri, Bruno and Leko, David and Genovese, Donatella and Grassucci, Eleonora and Van Looy, Jan-Willem and Mathe, Johan and Testa, Lucia and Beni{\'c}, Luka and Weber, Melanie and Zaghen, Olga and Vasylenko, Pavlo and Theiler, Raffael and Mahan, Scott and Khanal, Gaurav and S{\'a}nchez, Valentina and Loi, Dario and Onorato, Gabriele and Devadiga, Prasanna and Paik, Taejin and Velasco Sotomayor, Anier and M{\"u}ller, Jonas and Gupta, Aarav and Patel, Riyan and Mboya, Grold Otieno and Devadiga, Pushparaj and Gohain, Adam and Filipiak, Dominik and Banf, Michael and Mohammadian, Alireza and Akbari, Amirreza and Chaviano D{\'i}az, Gabriel and Ch{\'a}vez Mesa, Marcos and Grover, Ankit and Bourgerie, R{\'e}mi and Comparini, Alessio and Kumar, Ankit and Yusuf, Mohammad and Kaushal, Devesh and Orsi, Francesco and Mostofi, Ghazal and Lettl, Kolya and Khattami, Salwa and Franco, Gabriel and Falkouskaya, Yelizaveta and Franco, Jorge Luiz and Singla, Kajal and Espinosa I{\~n}iguez, Fernando David and Patil, Raj and Dolores-Cuenca, Eric and Petti, Luigi and Xu, Chenguang and Blando, Stefano and Zeh, Christine and Bastian, Lennart and Zheng, Kaden and Tiwari, Vihan and Athreya, Sud and Kajita, Hisataka and Micheron, Hugo and Devaux, R{\'e}mi and Jardin, Antoine and Emerson, Tegan and Fink, Olga and Miolane, Nina},
booktitle = {Proceedings of the 2nd Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2026)},
pages = {4--29},
year = {2026},
editor = {Berman, Eddie and Bern{\'a}rdez, Guillermo and Chen, Samantha and Cloninger, Alex and Doster, Timothy and Emerson, Tegan and Grigsby, J. Elisenda and Kvinge, Henry and Lawrence, Hannah and Marrinan, Tim and Myers, Audun and Papillon, Mathilde and Tahmasebi, Behrooz and Telyatnikov, Lev and Walters, Robin and Weber, Melanie and Xie, YuQing and Yeats, Eric},
volume = {334},
number = {2},
series = {Proceedings of Machine Learning Research},
month = {18--20 Aug},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v334/main/assets/bernardez26a/bernardez26a.pdf},
url = {https://proceedings.mlr.press/v334/bernardez26a.html}
}
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
Bachelor's thesis, Sapienza University of Rome (arXiv 2024)
Gaussian-process Bayesian optimization with Ax and BoTorch to tune CNN hyperparameters in fewer trials.
@misc{onorato2024bayesian,
title = {Bayesian Optimization for Hyperparameters Tuning in Neural Networks},
author = {Onorato, Gabriele},
year = {2024},
eprint = {2410.21886},
archivePrefix = {arXiv},
note = {Bachelor's thesis, Sapienza University of Rome}
}My Passions ❤️
In my free time, I enjoy reading 📖📰, playing video games 👾🕹️, and padel 🎾.
