Research
I am currently doing an internship at Samsung Research UK under the supervision of Umberto Michieli and Mete Ozay.
I hold a Ph.D. in Information Engineering from the University of Padova (Italy), where I was supervised by Prof. Pietro Zanuttigh. From May to November 2023, I was a research intern at Mila - Quebec AI Institute & Concordia University, working with Prof. Eugene Belilovsky.
My research focuses on large-scale machine learning problems and their applications to visual understanding. I have worked on various aspects of Federated Learning, Continual Learning, Domain Adaptation, and Semantic Segmentation. More recently, I have been exploring Model Merging and Image Generation.
[Download CV]
Publications
Preprints
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LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation |
Conferences
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Adaptive Local Training in Federated Learning |
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When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather |
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Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers |
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Asynchronous Federated Continual Learning |
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Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning |
Journals
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Federated Learning in Computer Vision |
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Continual coarse-to-fine domain adaptation in semantic segmentation |
* 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.