AE3S | University of luxembourg | Luxembourg

Rania Hamdani is a dynamic early-career research scientist specializing in software engineering, data management, and cloud architecture for Industry 5.0 applications. Currently based at the University of Luxembourg, she is engaged in advanced research on integrating heterogeneous data sources and optimizing decision-making in cloud-based systems. With a strong foundation in software development and operational research, Rania has already co-authored three research papers in Cloud-Edge AI and ontology-driven knowledge management. Her diverse technical skills span Python, Java, Docker, Kubernetes, and Azure DevOps, and she has gained international experience through roles in Luxembourg, Canada, France, and Tunisia. Passionate about both academic and applied innovation, she has contributed to multiple interdisciplinary projects in AI, human-computer interaction, and intelligent systems. Rania is also active in professional communities such as IEEE and youth science associations, reflecting her commitment to collaborative growth and scientific outreach.

Education Background

Rania Hamdani has a strong academic foundation rooted in engineering and scientific rigor. She earned her Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (ENSIT) between 2021 and 2024, where she specialized in Advanced Design, Service-Oriented Architecture, Object-Oriented Programming, Database Management, and Operational Research. Prior to that, she completed a Preparatory Cycle for Engineering Studies at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), focusing intensively on mathematics, physics, and core technology subjects—a rigorous program designed to prepare students for elite engineering schools. Rania also holds a Baccalaureate in Mathematics from Pioneer High School Bourguiba Tunis, where she graduated with distinction (Very Good) in 2019. This academic journey has laid a solid foundation for her multidisciplinary research and professional growth in software and data sciences.

Professional Experience

Rania Hamdani has developed a rich and diverse professional portfolio across academia and industry, with hands-on experience in software engineering, research, and cloud-based technologies. She is currently a Research Scientist at the University of Luxembourg (since November 2024), where she focuses on optimizing decision-making processes in cloud environments through advanced data integration techniques. Prior to this, she served as a Research Intern at the same institution (May to October 2024), contributing to projects in Ontology-Driven Knowledge Management and Cloud-Edge AI, resulting in three published papers. Alongside her academic work, Rania worked as a Part-Time Software Engineer at CareerBoosts in Canada (2021–2025), where she honed her skills in DevOps, data analysis, test automation, and backend development using tools like Python, Docker, and Kubernetes. Her earlier internships include roles at Qodexia (France), Sagemcom (Tunisia), and Tunisie Telecom, where she worked on smart recruitment platforms, employee management systems, and server monitoring tools using full-stack technologies such as SpringBoot, Angular, and PostgreSQL. This blend of research and industry experience positions Rania as a versatile and forward-thinking technology professional.

Research Interests of Rania Hamdani

Rania Hamdani’s research interests lie at the intersection of software engineering, operational research, data integration, and cloud-edge intelligence, with a strong orientation toward Industry 5.0 applications. She is particularly passionate about developing intelligent systems that enhance decision-making in cloud-based and distributed environments, leveraging AI, machine learning, and ontology-driven knowledge frameworks. Her work focuses on enabling seamless management of heterogeneous data sources, scalable architectures, and adaptive human-computer interaction (HCI) systems. Rania is also deeply engaged in exploring Cloud-Edge AI ecosystems, recommender systems, and automation pipelines using modern tools like Docker, Kubernetes, TensorFlow, and Neo4j. Her multidisciplinary approach reflects a vision for integrating research-driven insights with real-world industrial challenges, making her contributions both academically valuable and practically impactful.

Awards and Honors of Rania Hamdani

While still in the early stages of her research career, Rania Hamdani has demonstrated exceptional academic and technical promise. She graduated with a “Very Good” distinction in her Baccalaureate in Mathematics from the prestigious Pioneer High School Bourguiba in Tunis, reflecting her consistent academic excellence. Rania has also earned multiple professional certifications from Microsoft, including Azure Fundamentals, Azure Data Fundamentals, Azure AI Fundamentals, and Azure Security, Compliance, and Identity Fundamentals, showcasing her dedication to staying at the forefront of cloud and AI technologies. Though formal research awards or honors are not yet listed, her early publications, research contributions, and international internships highlight a trajectory poised for future recognition in both academic and industry spheres.

Conclusion

Rania Hamdani is highly suitable for the Best Emerging Researcher or Young Researcher Award category. She has excellent technical skills, promising early-stage research output, international exposure, and a forward-looking vision in areas like Industry 5.0, cloud-edge intelligence, and AI-based decision systems. While still building her publication track record and academic leadership, her current trajectory shows strong promise for future impactful contributions to scientific and industrial domains.

Duantengchuan Li | Computer Science and Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr Duantengchuan Li, School of Information Management, Wuhan University, Wuhan, China, Cahina

Assoc. Prof. Dr. Duantengchuan Li is a distinguished researcher at the School of Information Management, Wuhan University, China 🎓. His expertise spans Recommender Systems, Knowledge Graphs, Reinforcement Learning, Autonomous Driving, Large Language Models, and Computer Vision 🤖📊. With 40+ publications in top-tier journals and conferences such as IEEE TKDE, ACM TWEB, and AAAI 📜, Dr. Li has earned over 800 citations on Google Scholar 🌍. He has served as a Guest Editor for Electronics and a reviewer for premier journals, including IEEE TNNLS, IEEE TII, and Information Sciences 📝. Dr. Li’s impactful research contributions in AI and machine learning make him a leading expert in the field 🚀. His achievements include multiple national and provincial scholarships and a Bronze Medal in the “Internet+” Competition 🏅. His commitment to advancing AI-driven solutions for real-world applications makes him a strong candidate for the Best Researcher Award 🌟.

Publication Profile

Google Scholar

Education

Dr. Duantengchuan Li holds a Ph.D. in Computer Science from Wuhan University, China 🎓, where he specialized in AI-driven Recommender Systems and Knowledge Graphs 🤖📊. Prior to his Ph.D., he earned a Master’s degree from the Faculty of Artificial Intelligence in Education, Central China Normal University 🏫. His academic journey began with a Bachelor’s degree in Computer Science, where he honed his skills in machine learning, deep learning, and computational intelligence 💻. Throughout his education, he actively engaged in cutting-edge research and contributed to high-impact publications 📜. His strong academic foundation has paved the way for groundbreaking work in large-scale AI applications and intelligent systems 🚀. With an outstanding academic record and multiple scholarships, Dr. Li has established himself as a leading AI researcher, dedicated to advancing computational intelligence, knowledge-based systems, and deep learning architectures 🏆.

Experience

Dr. Duantengchuan Li is currently an Associate Researcher at the School of Information Management, Wuhan University, China 🏫. He has extensive experience in artificial intelligence, knowledge graphs, recommender systems, and deep learning 🤖. Dr. Li has been actively involved in academic publishing, serving as a Guest Editor for Electronics and as a reviewer for prestigious journals like IEEE TKDE, ACM TKDD, and IEEE TNNLS 📝. His research has been featured in top CCF A & B-ranked journals and conferences, including AAAI, ICWS, CAiSE, and IEEE Transactions 📊. Before joining Wuhan University, he completed his Ph.D. in Computer Science, contributing to AI-driven recommendation models 💡. His expertise extends to autonomous driving, reinforcement learning, and computer vision, and he continues to mentor young researchers in AI applications 🚀. His contributions in intelligent computing and AI research have made him a leading figure in his field 🌍.

Awards & Honors

Dr. Duantengchuan Li has received numerous accolades for his contributions to AI and computer science 🏆. In 2023, he led a team to win the Bronze Award in the prestigious “Internet+” Competition 🏅. His academic excellence was recognized with the National Scholarship (2019) 🎓 and the Provincial Outstanding Graduate Award (2017) 🏅. Additionally, he was honored with the Provincial Government Scholarship (2015) for his outstanding performance in research and academics 📜. Dr. Li also holds a Network Engineer Qualification Certification (2016), further demonstrating his technical expertise 💻. His contributions in AI research, particularly in deep learning, recommender systems, and autonomous driving, have earned him a spot among China’s top researchers 🚀. With 40+ high-impact publications and 800+ citations, Dr. Li’s work continues to shape the future of artificial intelligence and machine learning 🌟.

Research Focus

Dr. Duantengchuan Li’s research primarily focuses on Recommender Systems, Knowledge Graphs, Reinforcement Learning, Large Language Models, Autonomous Driving, and Computer Vision 🤖📊. His work explores efficient AI-driven recommendations, leveraging graph neural networks, deep learning, and sequential modeling to improve information retrieval 📜. He has also contributed to structured output evaluation for Large Language Models (LLMs), optimizing their prompt engineering and reasoning capabilities 💡. In autonomous driving, his research enhances intelligent vehicle navigation using deep reinforcement learning 🚗. Additionally, he has developed advanced cold-start QoS prediction models and multi-relation modeling for personalized recommendations 🔍. His work has been published in IEEE TKDE, ACM TOSEM, AAAI, and Information Sciences, demonstrating his cutting-edge innovations in AI applications 🚀. By integrating machine learning, knowledge graphs, and neural networks, Dr. Li continues to advance intelligent computing solutions for real-world problems 🌍.

Publication Top Notes

MFDNet: Collaborative poses perception and matrix Fisher distribution for head pose estimation

EDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system

Multi-perspective social recommendation method with graph representation learning

CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms

Knowledge graph representation learning with simplifying hierarchical feature propagation

Precise head pose estimation on HPD5A database for attention recognition based on convolutional neural network in human-computer interaction

Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation

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Ms. Rania Hamdani | Computer Science and Artificial Intelligence | Best Researcher Award

AE3S | University of luxembourg | Luxembourg

Professional Profile

Education Background

Professional Experience

Research Interests of Rania Hamdani

Awards and Honors of Rania Hamdani

Publications Top Noted

Conclusion