🦕 Donald Shenaj

I am a Postdoctoral Researcher at the University of Pisa.

I recently completed my Ph.D. at the University of Padova, under the guidance of Prof. Pietro Zanuttigh. My academic journey has been marked by collaborations with globally renowned institutions. From October 2024 to April 2025 I as a research intern at Samsung Research UK under the supervision of Umberto Michieli and Mete Ozay. From May 2023 to November 2023, I was also a research intern at Mila - Quebec AI Institute & Concordia University, working with Prof. Eugene Belilovsky.

In my Ph.D. I addressed key limitations of ML models for visual understanding, particularly their difficulty in adapting to dynamic, real-world conditions, learning over time and from decentralized data sources, focusing on three emerging paradigms: Domain Adaptation, Continual Learning, and Federated Learning. More recently, I have been exploring Model Merging, Image Generation and LLMs.

[Download CV]

Reviewer

ICCV 2025, CVPR 2025 (Outstanding Reviewer), ICLR Workshops 2025, CVPR Workshops 2025, WACV 2025, IEEE TPAMI, IEEE TMM, Pattern Recognition, CVIU, MMSP 2024, ICML 2023 Workshops, ICPR 2022, Harms and Risks of AI in the Military (HRAIM)

Publications

Preprints

LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
D. Shenaj, O. Bohdal, M. Ozay, P. Zanuttigh, U. Michieli
arXiv 2024.
[paper][code][video]

Conferences

	Adaptive Local Training in Federated Learning D. Shenaj, E. Belilovsky, P. Zanuttigh International Conference on Learning Representations (ICLR), 2025, MCDC Workshop. [paper]
	When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather G. Rizzoli, M. Caligiuri, D. Shenaj, F. Barbato, P. Zanuttigh IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025 (oral). [paper][code]
	Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers G. Rizzoli, D. Shenaj, P Zanuttigh IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Pretrain Workshop 2024 (oral). [paper]
	Asynchronous Federated Continual Learning D. Shenaj, M. Toldo, A. Rigon, P. Zanuttigh IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), FedVision Workshop, 2023 (oral). [paper][code]
	Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning D. Shenaj, E. Fanì, M. Toldo, D. Caldarola, A. Tavera, U. Michieli^†, M. Ciccone^†, P. Zanuttigh^†, B. Caputo^† IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 (oral). [paper][code]

Journals

	Federated Learning in Computer Vision D. Shenaj, G. Rizzoli, P. Zanuttigh IEEE Access, 2023. [paper]
	Continual coarse-to-fine domain adaptation in semantic segmentation D. Shenaj, F. Barbato, U. Michieli, P. Zanuttigh Elsevier Image and Vision Computing, 2022. [paper][webpage][code]

^* Equal contribution. ^† Equal supervision.

Ph.D., Master’s and Bachelor’s Thesis

[T3] D. Shenaj, “Morphing Distributed and Transfer Learning Paradigms for Visual Understanding”, Ph.D thesis in Information Engineering, University of Padova, (Submitted) December 2024.

[T2] D. Shenaj, “Coarse-to-Fine Learning for Semantic Segmentaion across Multiple Domains”, MS thesis in ICT for Internet and Multimedia, University of Padova, September 2021.

[T1] D. Shenaj, “Implementation and analysis of a vehicle counter system with Python and OpenCV”, BS thesis in Electronics Engineering for Energy and Information, University of Bologna, October 2019.