Francesco Agnelli | Graph Neural Networks | Best Researcher Award

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Dr. Francesco Agnelli | Graph Neural Networks | Best Researcher Award

Dr. Francesco Agnelli, University of Milan, Italy

Francesco Agnelli is an Italian researcher and PhD candidate at the University of Milan, where he delves into the frontiers of deep learning and artificial intelligence. Born in 1998 in Cantù, Italy, Francesco began his academic journey with a stellar Bachelor’s and Master’s degree in Mathematics, both earned cum laude at the University of Insubria. His academic focus evolved from Morse Theory to applied mathematics and computational intelligence, leading to his cutting-edge research on Graph Neural Networks (GNNs). Francesco's work bridges advanced machine learning methods with real-world problems like affective computing and graph isomorphism. With industry experience at Power Reply and teaching stints in local schools, he combines theory with practical impact. Francesco also shares his knowledge as a tutor and speaker in major academic and tech platforms, including an NVIDIA webinar and the 2024 ECCV Conference. He continues to contribute to the intersection of mathematics, AI, and neural computation.

Professional Profile 

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Summary of Suitability for the 'Research for Best Researcher Award'

Francesco Agnelli stands out as an exceptional candidate for the 'Research for Best Researcher Award' due to his impressive academic background, comprehensive research experience, and contributions to cutting-edge fields in mathematics and computer science, particularly through his work with Graph Neural Networks (GNNs).

Francesco holds a Master’s degree in Mathematics with honors (110/110 cum laude) from Università degli Studi dell'Insubria, where his academic pursuits focused on computational mathematics and machine learning. His work has specifically contributed to the application of GNNs in the domains of graph isomorphism and affective computing, demonstrating his ability to innovate within highly specialized research areas. His ongoing Ph.D. studies at Università degli studi di Milano further highlight his dedication to advancing human understanding through deep learning and multi-modal input integration.

Education 

Francesco Agnelli holds a Master’s degree in Mathematics with highest honors (110/110 cum laude) from the University of Insubria, where he focused on computational mathematics and machine learning. His thesis explored the application of Graph Neural Networks to the graph isomorphism problem. During his studies, he completed an Erasmus semester at KU Leuven, Belgium, earning top grades in advanced courses like Wavelets and Applications (29/30), Life Insurance (30/30), and Machine Learning (29/30). He also earned a Bachelor’s degree in Mathematics, again with 110/110 cum laude, from the same university. Francesco participated in a 24 CFU course for teacher training and holds a Scientific High School diploma from Liceo Scientifico Enrico Fermi with a score of 96/100. Now pursuing a PhD at the University of Milan, Francesco investigates the use of GNNs in affective computing, incorporating multimodal inputs and foundation models to advance human-centered AI.

Professional Experience

Francesco Agnelli is currently a PhD student at the University of Milan, conducting research at PhuseLab on deep learning and human understanding. His work integrates Graph Neural Networks and foundation models to process multimodal affective data. Previously, Francesco worked as an IT Consultant at Power Reply in Milan, where he supported the Eni Multicard CRM project. His tasks included transitioning the system from Siebel to Salesforce, performing data analysis, and implementing technical corrections using tools like SOQL and Excel. Earlier, he served as a Mathematics and Science teacher at Istituto Comprensivo Como Lora, where he engaged with younger students to foster scientific curiosity. In parallel, he has tutored university-level courses like Mathematical Analysis and Computational Mathematics. His technical fluency spans Python (especially PyTorch), Matlab, SOQL, and Java. Francesco’s diverse experiences reflect a strong ability to bridge academic rigor with real-world application in both corporate and educational environments.

Awards and Recognition

Francesco Agnelli has received consistent recognition throughout his academic and professional journey. He graduated cum laude in both his Bachelor’s and Master’s degrees in Mathematics, reflecting outstanding academic excellence. As a department representative and member of multiple university commissions at the University of Insubria, Francesco was honored for his leadership and advocacy in academic governance. He was also selected as a university tutor, mentoring students in Mathematical Analysis and Computational Mathematics. His expertise in artificial intelligence earned him an invitation to speak at the prestigious NVIDIA webinar on "Enhancing Visual Understanding With Generative AI". Furthermore, he was a volunteer at the renowned ECCV 2024 Conference, showcasing his commitment to engaging with the AI research community. These accolades affirm his growing impact in academia and the AI research landscape, positioning him as a promising thought leader in the field of Graph Neural Networks and beyond.

Research Skill On Graph Neural Networks

Francesco Agnelli’s research skills are deeply rooted in computational mathematics, with a specialized focus on Graph Neural Networks (GNNs). His academic evolution—from Morse Theory to graph isomorphism problems—demonstrates a solid foundation in abstract mathematics and its translation into real-world computing tasks. In his PhD at the University of Milan, he explores the fusion of GNNs with affective computing, particularly multimodal input processing and fine-tuning of foundation models. His technical toolkit includes Python (PyTorch), Matlab, and data query languages like SOQL. Francesco exhibits high proficiency in integrating theoretical algorithms with deep learning frameworks, often experimenting with cross-domain solutions. He has hands-on experience working with numerical analysis, approximation methods, and neural architectures, allowing him to simulate and interpret graph-structured data effectively. His research reflects a forward-thinking and collaborative approach to AI that bridges data, emotions, and decision-making through intelligent systems grounded in strong mathematical logic.

  Publication Top Notes

  • Title: KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis

  • Authors: Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

  • Journal: Array

  • DOI: 10.1016/j.array.2025.100392

  • Year: 2025

  • Citation: Agnelli, F., Facchi, G., Grossi, G., & Lanzarotti, R. (2025). KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis. Array. https://doi.org/10.1016/j.array.2025.100392

Jordi Rodeiro | Computer Science | Best Researcher Award

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Mr. Jordi Rodeiro | Computer Science | Best Researcher Award

 👤 Mr. Jordi Rodeiro, Institut de Recerca Sant Joan de Déu, Spain

Jordi Rodeiro Boliart is an accomplished International Computer Engineering and Sports Science graduate with a Master’s in Data Science and ongoing doctoral studies in Artificial Intelligence at La Salle Bonanova, Barcelona. Jordi is a dynamic professional blending a robust academic foundation with practical expertise. He is dedicated to leveraging data science and AI in health research, particularly autism prediction. With a deep passion for problem-solving and innovation, Jordi has conducted significant work in basketball analytics, biomedical data analysis, and medical imaging. His projects have included building Python tools, web applications, and dashboards that streamline decision-making. Jordi’s multilingual fluency in Catalan, Spanish, and English (C1) and his adaptability, critical thinking, and leadership skills underscore his commitment to excellence. As a mental health researcher, programming professor, and basketball coach, Jordi excels at interdisciplinary collaboration, fostering innovation, and making meaningful contributions to both academia and real-world applications.

Professional Profile

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🌟  Summary of Suitability for the Award

Jordi Rodeiro Boliart demonstrates an exceptional combination of academic excellence, multidisciplinary expertise, and impactful research, making him a strong candidate for the Research for Best Researcher Award. His academic journey spans multiple disciplines, including International Computer Engineering, Sports Science, and Data Science, culminating in a PhD in Artificial Intelligence and Autism Prediction. His diverse background equips him with a unique perspective in integrating technology, data science, and health research to address complex societal challenges.

Jordi’s research outputs reflect significant contributions to both applied and theoretical domains. Notably, his final master’s thesis focused on analyzing basketball data to enhance coaching strategies, while his degree project in the biomedical field led to a published scientific paper.

🎓 Education

Jordi Rodeiro Boliart boasts an impressive academic journey beginning with a dual degree in International Computer Engineering (La Salle, UPC) and Sports Science (INEFC Barcelona, UB). He further honed his expertise with a Master’s in Data Science (La Salle Bonanova, Barcelona), culminating in award-winning academic recognition. Currently pursuing a Ph.D. in Artificial Intelligence with a focus on autism prediction, Jordi demonstrates a commitment to cutting-edge research. His educational highlights include a final master’s thesis analyzing basketball data to enhance coaching strategies and a degree project in metabolomics published in a peer-reviewed journal. Jordi has also contributed to projects in medical imaging, such as using x-rays for illness detection. His academic journey is enriched by attending technology seminars at globally renowned institutions like Harvard and MIT, along with specialized training in leadership and organizational behavior. Jordi’s blend of technical and interdisciplinary studies defines his innovative, research-oriented career trajectory.

💼   Professional Experience

Jordi Rodeiro Boliart has a versatile professional background spanning research, teaching, and leadership. As a mental health researcher at Parc Sanitari Sant Joan de Déu, Jordi applies statistics and data science to critical health data, contributing to global assemblies and conferences. He serves as a university professor at La Salle Barcelona, teaching programming, mathematics, and IT software. As a data science intern at Sener, Jordi specialized in Power BI dashboards and analyzing corporate metrics. His engineering research internship included creating biomedical tools for metabolomic analysis, leading to a published paper. Jordi’s sports background complements his tech expertise, with roles as a basketball coach and coordinator, focusing on player development and team strategy. His earlier internships at Alfred Smart Systems and other engineering roles solidified his Python and gateway programming skills. Jordi’s diverse experiences exemplify his ability to integrate technology, data science, and education for impactful contributions.

🏅Awards and Recognitions

Jordi Rodeiro Boliart’s contributions have been widely recognized through various awards and honors. He received the prestigious Malaspina Award as part of the Empower consortium in 2023 and was a HackB finalist in the same year. Jordi was acknowledged with an academic excellence certificate for the best master’s record in Data Science (2023) and emerged as the LS Future Lab – Impact Challenge Hackathon winner in 2022. He represented his university as a National Model United Nations delegate in New York (2022) and participated in an international cooperation project in Perú. Jordi’s outstanding research on metabolomics earned him the opportunity to present at the Metabolomics 2022 conference. Beyond academia, Jordi is a certified Level II basketball coach, an FCBQ leadership trainee, and a master-certified Gannon Baker basketball coach. These accolades reflect his exceptional abilities in technical innovation, leadership, and interdisciplinary collaboration.

🌍  Research Skills On Computer Science

Jordi Rodeiro Boliart excels in applying advanced research methodologies to interdisciplinary challenges. His expertise includes data science, artificial intelligence, and object-oriented programming. Jordi has developed sophisticated tools for biomedical research, basketball analytics, and mental health studies. His doctoral research focuses on autism prediction through AI, combining statistical analysis and data visualization techniques. Jordi’s proficiency spans Python, MATLAB, MySQL, and Power BI, with skills in machine learning and medical image processing. He has designed Python programs to predict basketball outcomes, web apps for metabolomics, and diagnostic tools for x-rays. Jordi’s critical thinking, decision-making, and integrity define his research approach. His ability to present findings, such as at the Metabolomics 2022 conference, underscores his communication and analytical skills. Jordi’s research bridges academia and practical applications, demonstrating a commitment to addressing complex problems in health and technology.

📖 Publication Top Notes

1. The longitudinal relationship among physical activity, loneliness, and mental health in middle-aged and older adults: Results from the Edad con Salud cohort
  • Authors: Jordi Rodeiro, Beatriz Olaya, Josep Maria Haro, Aina Gabarrell-Pascuet, José Luis Ayuso-Mateos, Lea Francia, Cristina Rodríguez-Prada, Blanca Dolz-del-Castellar, Joan Domènech-Abella
  • Year: 2024
  • Citation: DOI: 10.1016/j.mhpa.2024.100667
2. The association of material deprivation with major depressive disorder and the role of loneliness and social support: A cross-sectional study
  • Authors: Joan Domènech-Abella, Carles Muntaner, Jordi Rodeiro, Aina Gabarrell-Pascuet, Josep Maria Haro, José Luis Ayuso-Mateos, Marta Miret, Beatriz Olaya
  • Year: 2024
  • Citation: DOI: 10.1016/j.jad.2024.09.071
3. Feasibility of an occupational e-mental health intervention for enhancing workplace mental health (EMPOWER RCT): Effectiveness and lessons learned (Preprint)
  • Authors: Carlota de Miquel, Christina M. Van der Feltz-Cornelis, Leona Hakkaart-van Roijen, Dorota Merecz-Kot, Marjo Sinokki, Jordi Rodeiro, Jennifer Sweetman, Kaja Staszewska, Ellen Vorstenbosch, Daniele Porricelli et al.
  • Year: 2024
  • Citation: DOI: 10.2196/preprints.66041
4. Trends of use of drugs with suggested shortages and their alternatives across 52 real-world data sources and 18 countries in Europe and North America
  • Authors: Marta Pineda-Moncusí, Alexandros Rekkas, Álvaro Martínez Pérez, Angela Leis, Carlos Lopez Gomez, Eric Fey, Erwin Bruninx, Filip Maljković, Francisco Sánchez-Sáez, Jordi Rodeiro et al.
  • Year: 2024
  • Citation: DOI: 10.1101/2024.08.28.24312695
5. CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources
  • Authors: Jordi Rodeiro, Ester Vidaña-Vila, Joan Navarro, Roger Mallol
  • Year: 2023

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