Francesco Agnelli | Graph Neural Networks | Best Researcher Award

Posted on by sciencefather

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 

Orcid

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

Tajunisha N | Computer Science | Best Faculty Award

Posted on by sciencefather

Dr. Tajunisha N | Computer Science | Best Faculty Award

Dr. Tajunisha N, Sri Ramakrishna College of Arts & Science for Women, India

Dr. N. Tajunisha is a distinguished academician and researcher in Computer Science, currently serving as Professor and Head of the Department at Sri Ramakrishna College of Arts & Science for Women. With over 27 years of academic experience and 23 years of research expertise, she specializes in Data Mining, Machine Learning, Big Data Analytics, and Networks. She earned her Ph.D. from Mother Teresa Women’s University, Kodaikanal, and has been an influential figure in research and development. As a leader, she has held key positions such as Research Coordinator, IQAC Coordinator, and Institution Innovation Cell (IIC) President. Her contributions to academia include publishing research papers in prestigious journals, securing research funding, and mentoring Ph.D. scholars. Recognized with the Senior Educator and Scholar Award, she actively collaborates with institutions like IBM, Rently, and L&T EDUTECH. Her work continues to shape the future of Computer Science education and research.

Professional Profile

Scopus

Orcid

Google Scholar

Suitability of Dr. N. Tajunisha for the Research for Best Faculty Award

Dr. N. Tajunisha is a distinguished academic leader with a strong record of excellence in teaching, research, and institutional development. With 27 years of academic experience and 23 years of research expertise, she has significantly contributed to the fields of Data Mining, Machine Learning, Big Data Analytics, and Networks. As a Professor and Head of the Department of Computer Science at Sri Ramakrishna College of Arts & Science for Women, she has played a crucial role in shaping the institution’s academic and research landscape.

Her research contributions are noteworthy, with 34 journal publications, 25 conference papers, and 9 SCOPUS-indexed papers, along with securing Rs. 3.17 lakhs from UGC for a Minor Research Project and Rs. 76 lakhs for a DST-FIST project. Dr. Tajunisha’s role as a Ph.D. guide, having successfully mentored three doctoral scholars and currently supervising five more, reflects her dedication to research mentorship. Her collaborations with IBM, Rently, L&T EDUTECH, Easy Design System, and VConnect highlight her industry engagement, while her participation in Doctoral Committees, Board of Studies (BOS), and Programme Advisory Committees showcases her leadership in academic governance.

🎓 Education

Dr. N. Tajunisha has an extensive educational background in Computer Science and Mathematics. She earned her Ph.D. in Computer Science from Mother Teresa Women’s University, Kodaikanal, in 2013, focusing on advanced research methodologies. Before that, she completed her M.Phil. in Computer Science from Bharathiar University, where she developed expertise in data analysis and computational techniques. Her academic journey began with a Master of Computer Applications (MCA) and a Bachelor’s degree in Mathematics from Madurai Kamaraj University, providing her with a strong mathematical foundation essential for algorithm development and computational problem-solving. Her diverse academic background has equipped her with critical analytical skills, enabling her to contribute significantly to the fields of Data Mining and Machine Learning. Her education and continuous professional development have played a crucial role in her ability to drive research innovations and mentor future scholars in Computer Science.

💼 Professional Experience

Dr. N. Tajunisha has over 27 years of academic experience and 23 years in research, significantly shaping Computer Science education. As the Professor & Head of the Department of Computer Science at Sri Ramakrishna College of Arts & Science for Women, she has spearheaded numerous academic and research initiatives. From 2013 to 2018, she served as the Research Coordinator, facilitating advanced research projects and securing substantial funding, including a Rs. 76 lakh DST-FIST grant. She has also played a pivotal role as the IQAC Coordinator (2018-2022), ensuring institutional excellence. Additionally, she has served as the Institution Innovation Cell (IIC) President, fostering entrepreneurship and innovation. Her industry collaborations with IBM, Rently, and L&T EDUTECH have enriched student learning experiences. She has contributed as a Board of Studies member in multiple colleges and played an active role in doctoral committees and inspection commissions under Bharathiar University.

🏅 Awards and Recognition

Dr. N. Tajunisha’s contributions to academia have been widely recognized through numerous awards and honors. She received the prestigious Senior Educator and Scholar Award from NFED in 2017 for her outstanding contributions to Computer Science education. She has also been honored with the Best Paper Award at the IEEE International Conference held at Satyabhama University in 2010. Her research excellence is reflected in her extensive publication record, including nine Scopus-indexed journal papers. Her leadership in institutional development led to Sri Ramakrishna College of Arts & Science for Women achieving an A+ grade in NAAC Cycle II under her tenure as IQAC Coordinator. Additionally, she has been an invited session chair at international conferences and serves as a reviewer for top-tier journals. Her commitment to fostering innovation and research has positioned her as a thought leader in Data Mining, Machine Learning, and Big Data Analytics.

🌍 Research Skills On Computer Science

Dr. N. Tajunisha’s research expertise spans Data Mining, Machine Learning, Big Data Analytics, and Networks. She has successfully guided three Ph.D. scholars and is currently mentoring five more, contributing to advancements in computational intelligence and predictive analytics. Her research has secured substantial funding, including a Rs. 3.17 lakh UGC Minor Research Project and Rs. 76 lakh for a DST-FIST project. With 34 journal papers, 25 conference papers, and two books to her credit, she has made significant contributions to her field. She actively collaborates with industry leaders like IBM, Rently, and L&T EDUTECH, ensuring practical applications of her research. Her ability to integrate academic knowledge with real-world solutions makes her a leading researcher in her domain. She has also been a reviewer for international journals and a committee member in doctoral research evaluations, further enhancing her impact in the field.

📖 Publication Top Notes

  • Performance analysis of k-means with different initialization methods for high-dimensional data
    Author(s): VSN Tajunisha
    Journal: International Journal of Artificial Intelligence and Applications
    Citations: 35
    Year: 2010

  • An efficient method to improve the clustering performance for high dimensional data by principal component analysis and modified K-means
    Author(s): N Tajunisha, V Saravanan
    Journal: International Journal of Database Management Systems
    Citations: 20
    Year: 2011

  • An increased performance of clustering high dimensional data using Principal Component Analysis
    Author(s): N Tajunisha, V Saravanan
    Conference: 2010 First International Conference on Integrated Intelligent Computing
    Citations: 19
    Year: 2010

  • A study on evolution of data analytics to big data analytics and its research scope
    Author(s): S Sruthika, N Tajunisha
    Conference: 2015 International Conference on Innovations in Information, Embedded and …
    Citations: 14
    Year: 2015

  • Predicting Student Performance Using MapReduce
    Author(s): N Tajunisha, M Anjali
    Journal: International Journal of Emerging and Computer Science
    Citations: 14
    Year: 2015

  • A new approach to improve the clustering accuracy using informative genes for unsupervised microarray data sets
    Author(s): N Tajunisha, V Saravanan
    Journal: International Journal of Advanced Science and Technology
    Citations: 10
    Year: 2011

  • Automatic classification of ovarian cancer types from CT images using deep semi-supervised generative learning and convolutional neural network
    Author(s): N Nagarajan, P.H. Tajunisha
    Journal: Revue d’Intelligence Artificielle
    Citations: 9
    Year: 2021

  • Classification of cancer datasets using artificial bee colony and deep feed-forward neural networks
    Author(s): M Karunyalakshmi, N Tajunisha
    Journal: International Journal of Advanced Research in Computer and Communication …
    Citations: 8
    Year: 2017

  • Concept and Term-Based Similarity Measure for Text Classification and Clustering
    Author(s): B Sindhiya, N Tajunisha
    Journal: IJERST
    Citations: 7
    Year: 2014

  • Optimal Parameter Selection-Based Deep Semi-Supervised Generative Learning and CNN for Ovarian Cancer Classification
    Author(s): PH Nagarajan, N Tajunisha
    Journal: ICTACT Journal on Soft Computing
    Citations: 5
    Year: 2023

SAMEERCHAND PUDARUTH | Artificial Intelligence | Academic Excellence Award

Posted on by sciencefather

Assoc. Prof. Dr. SAMEERCHAND PUDARUTH | Artificial Intelligence | Academic Excellence Award

Assoc. Prof. Dr. SAMEERCHAND PUDARUTH, University of Mauritius, Mauritius

Dr. Sameerchand Pudaruth is an Associate Professor in the Faculty of Information, Communication & Digital Technologies at the University of Mauritius. With a PhD in Artificial Intelligence, his expertise spans machine learning, computational linguistics, and data science. His academic journey reflects a deep commitment to technological advancement and knowledge dissemination. Having served as Head of the ICT Department (2019–2021), he played a key role in shaping academic policies and research initiatives. Dr. Pudaruth is an accomplished author and a sought-after researcher, contributing significantly to AI, distributed systems, and digital transformation. His membership in prestigious organizations such as IEEE, ACM, and IAENG highlights his standing in the global research community. Beyond academia, he is dedicated to mentoring students, fostering innovation, and driving interdisciplinary collaboration in AI. His work continues to bridge the gap between theoretical research and practical AI applications, making significant strides in Mauritius and beyond.

Professional Profile

Scopus 

Orcid

Google Scholar

Suitability for the Research for Best Researcher Award – Associate Professor Dr. Sameerchand Pudaruth

Dr. Sameerchand Pudaruth is a distinguished academic and researcher in Artificial Intelligence, Computer Science, and related fields, with a strong background in teaching, research, and leadership. With over two decades of experience, he has contributed significantly to the advancement of AI, distributed systems, multimedia, and computational linguistics. His PhD in Artificial Intelligence, combined with his legal studies, positions him uniquely at the intersection of AI and law, demonstrating an interdisciplinary approach to research.

His extensive publication record, including book chapters, conference proceedings, and journal articles, highlights his dedication to pushing the boundaries of AI, machine learning, and computational methodologies. His research contributions in legal text classification, intelligent systems, and IT support services optimization have real-world applications, making his work both academically rigorous and practically relevant. His leadership roles, including serving as Head of the ICT Department and holding senior positions in IEEE and ACM, reflect his commitment to advancing research and fostering collaboration in the global research community.

🎓 Education

Dr. Sameerchand Pudaruth holds a PhD in Artificial Intelligence from the University of Mauritius, awarded in 2020, focusing on AI-driven decision-making systems. His academic portfolio includes an MSc in Computer Science with a specialization in Distributed Systems and Multimedia (2004–2006) and a BSc (Hons) in Computer Science and Engineering (First Class) from the same institution (2000–2003). In addition, he pursued legal studies, earning an LLB from the University of London (2009–2012) and completing the Law Practitioner’s Vocational Course (2013). This diverse educational background equips him with a unique interdisciplinary perspective, blending AI, computational intelligence, and legal analytics. His continuous professional development is evident through active participation in research forums, conferences, and international collaborations. Dr. Pudaruth’s academic journey reflects a commitment to lifelong learning, innovation, and thought leadership in artificial intelligence and digital technologies.

💼 Professional Experience

Dr. Sameerchand Pudaruth has been an integral part of the University of Mauritius since 2007. Currently serving as an Associate Professor (since 2022), he previously held the positions of Senior Lecturer (2016–2022) and Lecturer (2007–2016). He also served as Head of the ICT Department (2019–2021), where he spearheaded academic advancements and research collaborations. His career is marked by expertise in AI, machine learning, and data-driven technologies, leading various projects focused on intelligent systems. He has mentored numerous postgraduate students and contributed extensively to curriculum development. Dr. Pudaruth’s industry collaborations have reinforced his reputation as a pioneer in applied AI research. His leadership in conferences and symposiums underscores his commitment to advancing knowledge in computational sciences. As an educator, researcher, and innovator, he continuously pushes the boundaries of AI applications, influencing both academia and industry.

🏅 Awards and Recognition

Dr. Sameerchand Pudaruth’s contributions to AI and computational research have earned him numerous accolades. He has been a Senior IEEE Member since 2020, recognizing his significant professional achievements. His role as a founding member and Vice-Chair of IEEE Mauritius Section (2018–2021) highlights his leadership in fostering AI research communities. He has received research grants and recognition for outstanding publications in AI, machine learning, and computational linguistics. His book “Python in One Week” (2010) and multiple Springer book chapters have cemented his reputation in AI education. His work on machine learning applications in law and data science has been widely cited, further amplifying his global impact. His membership in ACM, IAENG, and the Association for Computational Linguistics reflects his standing in the international AI research community. Through groundbreaking research, academic leadership, and scholarly contributions, Dr. Pudaruth continues to be a driving force in AI innovation.

🌍 Research Skills On Artificial Intelligence

Dr. Sameerchand Pudaruth specializes in artificial intelligence, machine learning, and computational linguistics. His expertise spans natural language processing, legal informatics, and AI-driven decision-making models. He has pioneered research in AI applications for law, healthcare, and distributed systems. With a deep understanding of neural networks, deep learning, and intelligent automation, he has contributed significantly to AI-driven knowledge systems. His work includes feature selection using evolutionary algorithms, predictive modeling, and sentiment analysis. He has led interdisciplinary collaborations to advance AI ethics, data privacy, and algorithmic fairness. His ability to translate complex AI concepts into real-world applications has led to impactful research publications. As a member of international AI organizations, he remains at the forefront of technological advancements, constantly exploring innovative solutions. Dr. Pudaruth’s research continues to influence academia, industry, and public policy, making AI more accessible and applicable across diverse domains.

 📖 Publication Top Notes

  1. Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers
    • Authors: T. Munisami, M. Ramsurn, S. Kishnah, S. Pudaruth
    • Citations: 262
    • Year: 2015
  2. People factors in agile software development and project management
    • Authors: V. Lalsing, S. Kishnah, S. Pudaruth
    • Citations: 195
    • Year: 2012
  3. Predicting the price of used cars using machine learning techniques
    • Authors: S. Pudaruth
    • Citations: 183
    • Year: 2014
  4. Automatic Recognition of Medicinal Plants using Machine Learning Techniques
    • Authors: A. Begue, V. Kowlessur, U. Singh, F. Mahomoodally, S. Pudaruth
    • Citations: 123
    • Year: 2017
  5. A review of mobile ad hoc NETwork (MANET) Protocols and their Applications
    • Authors: D. Ramphull, A. Mungur, S. Armoogum, S. Pudaruth
    • Citations: 98
    • Year: 2021
  6. Authorship attribution using stylometry and machine learning techniques
    • Authors: H. Ramnial, S. Panchoo, S. Pudaruth
    • Citations: 71
    • Year: 2016
  7. Sentiment analysis from Facebook comments using automatic coding in NVivo 11
    • Authors: S. Pudaruth, S. Moheeputh, N. Permessur, A. Chamroo
    • Citations: 53
    • Year: 2018
  8. Forgotten, excluded or included? Students with disabilities: A case study at the University of Mauritius
    • Authors: U. Singh, S. Pudaruth, R. Gunputh
    • Citations: 30
    • Year: 2017
  9. Resource allocation in 4G and 5G networks: A review
    • Authors: L.N. Degambur, A. Mungur, S. Armoogum, S. Pudaruth
    • Citations: 29
    • Year: 2021
  10. SuperFish: A Mobile Application for Fish Species Recognition using Image Processing Techniques and Deep Learning
  • Authors: P. Sameerchand, N. Nadeem, A. Chandani, K. Somveer, V. Munusami, S. Pudaruth
  • Citations: 26
  • Year: 2021

Jordi Rodeiro | Computer Science | Best Researcher Award

Posted on by sciencefather

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

Orcid

Google Scholar

🌟  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

.