Alberto G. Valerio

I'm Alberto G. Valerio

Ph.D. student in Digital Innovation and e-Health, a interdisciplinary and cutting-edge doctoral program focused on the design, development, and assessment of advanced AI-based technologies in the healthcare field.

About

alberto g valerio phd student

Hello,

I'm Alberto G. Valerio, a Ph.D. student in Digital Innovation and e-Health at the Department of Computer Science, University of Bari "Aldo Moro" (Italy), under the guidance of Prof. Giovanna Castellano, Dr. Gabriella Casalino, and Dr. Gennaro Vessio from the Computational Intelligence Laboratory (CILab).

I began my academic journey with a Bachelor's degree in Informatics and Technologies for Software Production, graduating with honors (110/110 cum laude). I then completed my Master's in Computer Science with a focus on Artificial Intelligence, also with distinction (110/110 cum laude and special mention). This strong academic background has not only strengthened my technical skills but has also fueled my interest in leveraging cutting-edge technologies, especially in computer vision and generative large language models.

In my doctoral research, I am exploring the potential of deep learning models to enhance medical diagnostics, treatment planning, and patient care within the realms of preventive and precision medicine, as well as neuroimaging. A key focus of my work is on explainability, which aims to make AI predictions more transparent and interpretable for healthcare professionals. Currently, I am developing models that integrate explainability with high-performance deep learning techniques, specifically for brain tumor segmentation and early detection of Alzheimer's disease, to ensure that the results are both accurate and actionable in clinical scenarios.

Before beginning my journey in research and academia and a new chapter of my life, I spent over 10 years working as a web software engineer. This professional background provided me with extensive industry experience in software development as well as practical problem-solving, which now enhances my approach to research and innovation.

Feel free to explore my website to learn more about my research, publications, and ongoing projects. I am always open to connect with researchers, clinicians, and anyone interested in the transformative power of AI and digital health.

Expertise

My research spans multiple interconnected domains and areas of interest, combining theoretical foundations with practical implementations. I leverage modern frameworks and libraries to develop innovative solutions, focusing on both established and emerging technologies. Through my work, I aim to bridge theoretical concepts with real-world applications, contributing to the advancement of the digital health while addressing contemporary challenges.

computer vision

95%

deep learning

95%

explainable ai

85%

large language models

75%

medical imaging

75%

green ai

60%
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scikit learn logo
opencv logo
seaborn logo
matplotlib logo
numpy logo
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Research

Below you will find a selection of my recent research contributions to peer-reviewed journals and conference proceedings. These publications reflect my ongoing work in advancing the field and collaborating with fellow researchers. Each entry includes links to the full text where available, along with any associated code repositories or supplementary materials.

2025 - Conference

Exploring the Expressive Power of Large Language Models in Neuro-Fuzzy System Explainability: A Study on EEG-Based Seizure Detection

G. Casalino, G. Castellano, D. Margherita A. G. Valerio, G. Vessio, G. Zaza

Paper / Cite us

BibTeX Citation

@inproceedings{casalino2025llmneurofuzzy,
	author={Gabriella Casalino and Giovanna Castellano and Daniele Margherita and Alberto Gaetano Valerio and Gennaro Vessio and Gianluca Zaza},
	title={Exploring the Expressive Power of Large Language Models in Neuro-Fuzzy System Explainability: A Study on EEG-Based Seizure Detection},
	booktitle={Proceedings of the Second Workshop on Explainable Artificial Intelligence for the Medical Domain},
	series={CEUR Workshop Proceedings},
	volume={4059},
	year={2025},
	publisher={CEUR-WS.org},
	url={https://ceur-ws.org/Vol-4059/paper15.pdf}
} 
2025 - Journal

From Segmentation to Explanation: Generating Textual Reports from MRI with LLMs

A. G. Valerio, K. Trufanova, S. de Benedictis, G. Vessio, G. Castellano

Paper / Code / Demo / Cite us

BibTeX Citation

@article{valerio2025segmentation,
	title={From segmentation to explanation: Generating textual reports from MRI with LLMs},
	journal={Computer Methods and Programs in Biomedicine},
	volume={270},
	pages={108922},
	year={2025},
	issn={0169-2607},
	doi={https://doi.org/10.1016/j.cmpb.2025.108922},
	url={https://www.sciencedirect.com/science/article/pii/S0169260725003396},
	author={Alberto G. Valerio and Katya Trufanova and Salvatore {de Benedictis} and Gennaro Vessio and Giovanna Castellano}
} 
2025 - Conference

Enhancing the Explainability of Neuro-Fuzzy Systems with Large Language Models: A Case Study on EEG-Based Epileptic Seizure Classification

G. Casalino, G. Castellano, A. G. Valerio, G. Vessio, G. Zaza

2025 - Conference

Generative AI Across Modalities: Insights from Our Research on Domain-Aware Content

G. Castellano, E. Colonna, N. Fanelli, L. Laraspata, I. Rinaldi, A. G. Valerio, G. Vessio

2025 - Conference

RecSys CarbonAtor: Predicting Carbon Footprint of Recommendation System Models

G. Spillo, A. G. Valerio, F. Franchini, A. De Filippo, C. Musto, M. Milano, G. Semeraro

Paper / Code / Cite us

BibTeX Citation

@inproceedings{spillo2025carbonator,
	author={Spillo, Giuseppe and Valerio, Alberto Gaetano and Franchini, Felice and De Filippo, Allegra and Musto, Cataldo and Milano, Michela and Semeraro, Giovanni},
	editor={Boratto, Ludovico and De Filippo, Allegra and Lex, Elisabeth and Ricci, Francesco},
	title={RecSys CarbonAtor: Predicting Carbon Footprint of Recommendation System Models},
	booktitle={Recommender Systems for Sustainability and Social Good},
	year={2025},
	publisher={Springer Nature Switzerland},
	address={Cham},
	pages={98--110},
	isbn={978-3-031-87654-7}
} 

News

Stay updated with my latest academic activities, including conference presentations, workshop participations, and research milestones. This section features recent achievements, upcoming events, and notable developments in my research journey. I regularly update this space to share significant moments and opportunities for collaboration.

Download

As part of my doctoral program in Digital Innovation and e-Health, a major attention is dedicated to supervising student theses, guiding project work, and assisting in teaching. In this section, you will find a selection of useful resources, including seminar presentations, teaching materials, and other educational content. Feel free to explore this repository and use any of the available materials to support your own research or educational endeavors.

# Filename Type Actions
1 Thesis_Template_LaTeX.zip resource File info Download
2 An_Introduction_To_LLMs.pdf lecture File info Download

File information

A thesis template in English, mainly intended for master students, developed in LaTeX. The file contains useful tips, guidelines and formatting rules to follow for a well-crafted thesis work. The file can be processed by any online or local LaTeX compiler (e.g., Overleaf, MacTeX, TexLive).

Special thanks go to Dr. Gennaro Vessio for his tireless work and valuable contribution in making this file available.

File information

This handout accompanies a seminar delivered to undergraduate students, introducing the foundational concepts behind large language models (LLMs). It covers key topics such as neural networks, transformer architectures, training methods, and real-world applications of LLMs. Designed to be accessible to students with limited prior knowledge of machine learning.

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    Contact

    I welcome opportunities for academic discussion, collaboration, and exchange of ideas. Whether you're interested in my research, seeking collaboration opportunities, or have questions about my work, feel free to reach out using the form below or through my institutional email. I strive to respond to all inquiries promptly and look forward to connecting with fellow researchers and professionals.

    Institutional email

    a.valerio31@phd.uniba.it
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