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I am a post-doctoral researcher<\/strong> at the Department of Computer Science, M<\/span>odeling, Electronics and Systems Engineering (DIMES<\/a>) of the University of Calabria<\/a>, and a member of the Scalable Computing and Cloud Laboratory (SCALab<\/a>). I obtained my Ph.D. in Information and Communication Technologies (ICT) from DIMES in January 2025. From July to December 2023, I was a visiting researcher at the Department of Computer Science and Technology<\/a> of the <\/span>University of Cambridge, collaborating with the Mobile Systems Research Lab<\/a>. <\/span>Previously, I was a research fellow at DIMES from January to November 2021. I received my Master’s degree in Computer Engineering (Big Data Analytics and Data Science) in 2020 and my Bachelor’s degree in Computer Engineering in 2018, both from DIMES. Since 2022, I have also been a contract professor at DIMES (see Teaching<\/a>).<\/p>\nCurrently, my research interests include:<\/p>\n
- \n
- Edge AI and Tiny Machine Learning<\/li>\n
- Bias, fairness, and sustainability in Large Language Models (LLMs)<\/li>\n
- Edge-cloud continuum architectures<\/li>\n
- Big Data and high-performance computing (HPC)<\/li>\n<\/ul>\n
I serve as a reviewer for international conferences and journals concerning artificial intelligence, parallel and distributed computing, and big data (see Service<\/a>).<\/p>\n
I am also a co-author of the book “Programming Big Data Applications: Scalable Tools and Frameworks for Your Needs<\/a>” – World Scientific Publishing (2024) – which delves into models, programming frameworks, and algorithms for processing and analyzing large volumes of data.<\/p>\n
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<\/p>\nSelected publications<\/h2>\n
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge<\/a> \u2014 Machine Learning, 2025<\/em><\/p>\n
TinyTTA: Efficient Test-time Adaptation for Edge Devices via Early-exit Ensembles<\/a> \u2014 NeurIPS 2024<\/em><\/p>\n
Xai-driven knowledge distillation of large language models for efficient deployment on low-resource devices<\/a> \u2014 Journal of Big Data, 2024<\/em><\/p>\n