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

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

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.

Professional Profile 

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.

Publications Top Noted

Title: Adaptive human‑computer interaction for Industry 5.0: A novel concept, with comprehensive review and empirical validation
Year: 2025

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.

Yue Wu | Machine Learning | Best Researcher Award

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Yue Wu | Machine Learning | Best Researcher Award

Assist. Prof. Dr Yue Wu, Hangzhou Dian, China

Assist. Prof. Dr. Yue Wu is a promising young academician whose work bridges the gap between automation, machine learning, and electronic design automation. Currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, China, he exemplifies research excellence through his interdisciplinary expertise. He earned his Ph.D. from Zhejiang University in Aeronautics and Astronautics and a B.S. from Wuhan University of Technology in Automation. His scholarly interests focus on logic synthesis, physical design, and intelligent prediction algorithms using graph neural networks. Despite his early career stage, Dr. Wu has demonstrated impactful contributions to both academia and industry-relevant applications. His recent publication on pre-routing slack prediction using graph attention networks stands out as a novel solution in the realm of EDA. With a strong academic foundation and active research output, Dr. Wu is a fitting nominee for the Best Researcher Award, representing the next generation of innovation in AI-driven engineering.

Publication Profile

Orcid

Education

Dr. Yue Wu has a solid educational foundation in engineering and automation. He earned his Bachelor of Science (B.S.) in Automation from the Wuhan University of Technology in 2018. There, he developed a robust understanding of control systems, signal processing, and computational modeling. Pursuing his academic passion, he undertook doctoral studies at the School of Aeronautics and Astronautics, Zhejiang University, one of China’s premier research institutions. He completed his Ph.D. in 2023, focusing on interdisciplinary topics combining aeronautical engineering, data science, and intelligent systems. His doctoral work incorporated advanced machine learning techniques and their applications in hardware-aware environments, preparing him to lead novel research at the intersection of automation and electronics. This strong academic background equips him with the theoretical depth and practical experience essential for future-forward research in intelligent systems and electronic design automation.

Experience

Dr. Yue Wu is currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, since 2023. Despite being in the early phase of his academic career, he has demonstrated exceptional scholarly promise through teaching, mentorship, and high-impact research. His role involves designing and delivering advanced courses on machine learning, logic circuits, and digital system design while actively supervising undergraduate and graduate research projects. He collaborates with interdisciplinary teams, focusing on the integration of machine learning techniques into physical design and logic synthesis processes, bridging hardware and AI innovations. Prior to this, he was involved in multiple research projects at Zhejiang University during his Ph.D., contributing to algorithm development and experimental validation of graph-based learning techniques. Dr. Wu’s combined expertise in automation, EDA tools, and machine learning positions him as a rising leader in academic research and technological advancement.

Awards and Honors

As a rising scholar, Dr. Yue Wu has been recognized for his academic achievements and research contributions. While specific institutional or national awards are yet to be recorded in the public domain, his selection as a faculty member at Hangzhou Dianzi University, known for its emphasis on electronic and information technology research, is a testament to his academic caliber. His recent first-author publication in the peer-reviewed journal “Automation” (2025) highlights his research excellence and innovation in the application of graph attention networks to pre-routing slack prediction, a complex problem in VLSI design. Additionally, his collaborative projects during his Ph.D. at Zhejiang University received internal recognition and contributed to multiple research grants. Dr. Wu’s research profile is steadily growing, and he is well on the path toward future accolades at the national and international levels as he continues to publish and lead in cutting-edge interdisciplinary domains.

Research Focus

Dr. Yue Wu’s research focuses on the intersection of machine learning and electronic design automation (EDA). His primary interest lies in developing intelligent systems that enhance the physical design and logic synthesis processes used in integrated circuit (IC) design. By leveraging advanced models like graph neural networks (GNNs) and attention-based architectures, Dr. Wu aims to address critical challenges such as slack prediction, timing analysis, and routing optimization. His expertise also extends to hardware-aware machine learning, wherein algorithmic efficiency is optimized for real-world applications in chip manufacturing. His recent work—“Pre-Routing Slack Prediction Based on Graph Attention Network”—demonstrates his ability to combine theoretical AI models with practical EDA problems. By pushing the boundaries of design automation through AI integration, Dr. Wu contributes to faster, smarter, and more power-efficient chip design—critical for the next generation of computing devices. His vision is to make intelligent design automation a core component of future electronics engineering.

Publication Top Notes

Pre-Routing Slack Prediction Based on Graph Attention Network

 

Toktam Akbari Khalaj | Data Science and Analytics | Excellence in Research Award

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Toktam Akbari Khalaj | Data Science and Analytics | Excellence in Research Award

Mrs Toktam Akbari Khalaj, Mashhad University of Medical Sciences, Iran

Mrs. Toktam Akbari Khalaj is a distinguished Iranian biostatistician known for her rigorous research in traffic injury prediction, prehospital emergency care, and health data modeling. With over two decades of professional experience and a dynamic academic career, she has significantly contributed to public health research, especially in North-Eastern Iran. Her publications, presented in reputed journals like Heliyon and BMC Public Health, emphasize the application of advanced statistical and machine learning techniques to address real-world health challenges. She serves as a project manager and biostatistician at Emergency Medical Services, and her proactive approach to forecasting health emergencies during crises like COVID-19 has drawn national attention. Fluent in both Persian and English, Mrs. Akbari Khalaj combines analytical excellence with outstanding communication and leadership skills. Her interdisciplinary approach and practical contributions make her an exceptional nominee for the Excellence in Research Award.

Publication Profile

Google Scholar

Education

Mrs. Akbari Khalaj holds a Master of Science (M.Sc.) in Biostatistics from Mashhad University of Medical Sciences (2017–2021), where she conducted a thesis on forecasting ambulance dispatches for traffic accidents using time series regression models. Prior to that, she earned a Bachelor of Science (B.Sc.) in Statistics from Azad University of Mashhad (2000–2004). Her academic background provided a solid foundation in statistical modeling, data mining, and biostatistical applications in health systems. Her postgraduate training enabled her to develop specialized expertise in predictive analytics and epidemiological modeling, particularly in trauma and emergency settings. Her advanced research skills have led her to publish and collaborate across multiple health domains. She also holds a TOEFL iBT score of 101, reflecting strong English proficiency. These academic credentials, combined with her practical research, reflect her dedication to applying theoretical insights into meaningful public health solutions.

Experience

Mrs. Akbari Khalaj brings a wealth of professional experience in statistical analysis and healthcare research. She began as a statistician at Emergency Medical Services (EMS) in 2007, transitioning into a biostatistician role in 2017. Since 2021, she has served as a project manager at EMS, overseeing data-driven projects in health surveillance, prehospital care, and trauma analytics. She has also worked as a researcher at Azad University (2002–2004) and Mashhad University of Medical Sciences (2018–2022), contributing to both academic and operational research. Her expertise in handling large datasets, implementing time series and logistic regression models, and her proficiency in R, Stata, SPSS, and Power BI, make her a cornerstone in data-informed decision-making. Beyond technical prowess, she is known for her leadership in emergency response analytics and evidence-based planning, especially during mass gatherings and pandemic crises. Her multidisciplinary involvement makes her an ideal candidate for research distinction.

Awards and Honors

While specific awards or formal recognitions are not explicitly listed, Mrs. Akbari Khalaj’s academic and professional contributions reflect honor-worthy distinction. Her co-authored and first-authored publications in high-impact journals such as BMC Public Health and Heliyon indicate peer recognition and scholarly influence. She has presented at prestigious events like the International Congress on Health in Arbaeen and the International Congress on Prehospital Emergency Innovation, signifying academic acknowledgment. Her long-standing role as project manager at EMS and her collaborative research with top Iranian health institutions underscore her leadership and credibility. Notably, her involvement in managing and modeling public health emergencies, such as during COVID-19, highlights the real-world impact of her work. These accomplishments reflect an ongoing trajectory of professional recognition that aligns with the spirit of the Excellence in Research Award.

Research Focus

Mrs. Akbari Khalaj’s research is rooted in biostatistics and emergency health systems, with a core focus on time series modeling, logistic regression, and big data analytics in traffic and trauma-related health incidents. She has applied innovative statistical methods to predict ambulance dispatches, assess injury mortality, and evaluate prehospital emergency trends in urban Iran. Her work addresses the intersection of public health, emergency response, and predictive modeling, often exploring the effects of external factors like COVID-19. Additionally, she is engaged in meta-analysis, count data modeling, machine learning, and data visualization for public health monitoring. Her research is both preventive and responsive, aiming to enhance policy decisions and emergency service planning. By integrating advanced computational tools with field data, Mrs. Akbari Khalaj’s work significantly contributes to predictive healthcare and operational efficiency. Her research output supports a data-driven approach to public health crisis management.

Publication Top Notes

Multiple-scale spatial analysis of paediatric, pedestrian road traffic injuries in a major city in North-Eastern Iran 2015–2019

Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice

Comparison of GAP, R-GAP, and new trauma score (NTS) systems in predicting mortality of traffic accidents that injure hospitals at Mashhad University of medical sciences

An exploration of occupational violence among emergency medical staff of a major medical university; 2020

Evaluation of Prehospital Emergency Medical Services before and after COVID-19 in Mashhad

GENERAL WIEW OF COMPARISON BETWEEN SMART BOARD & BLACK BOARD IN GENERAL MATHEMATHICS BOOK 1 & 2 AMONG IRANIAN HIGH SCHOOL

Analysis of the Patterns of Mortality Causes in Traffic Accident Injuries Using Logistic Regression Model in Northeastern Iran