Deniz Mertkan Gezgin | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Assoc. Prof. Dr. Deniz Mertkan Gezgin | Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Deniz Mertkan Gezgin, Trakya University, Turkey

Assoc. Prof. Dr. Deniz Mertkan Gezgin is a distinguished academic specializing in Cyberpsychology, Artificial Intelligence, Educational Technology, and Human-Computer Interaction. Born in Kütahya, Turkey, he completed his undergraduate degree in Computer Engineering from Çanakkale Onsekiz Mart University in 1999, followed by a master’s and PhD from Trakya University. He pursued postdoctoral research at the Middle East Technical University, focusing on nomophobia among university students. Dr. Gezgin has held academic positions in institutions such as Zagreb University, University of Milan, and Prizren University, and currently serves as an Associate Professor in Computer Engineering at Trakya University. His research addresses digital behavior issues such as smartphone addiction, FoMO, and cyberloafing, with over a dozen SSCI-indexed publications. Beyond academia, he actively contributes to addiction prevention through university commissions and youth advisory roles. His interdisciplinary work bridges technology and psychology, making significant contributions to digital well-being and educational innovation.

Publication Profile

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Educational Background

Assoc. Prof. Dr. Deniz Mertkan Gezgin has a strong and diverse academic background rooted in computer engineering and educational technologies. He completed his postdoctoral studies in 2016 at the Middle East Technical University, specializing in Computer and Instructional Technologies Education. His postdoctoral thesis focused on the prevalence of nomophobia among university students in Turkey. Dr. Gezgin earned his PhD in 2011 from Trakya University in Computer Engineering, where he investigated wireless network technologies and encryption systems. He also holds a master’s degree from Trakya University, completed in 2006, with a thesis on e-exam applications using ASP and ASP.NET technologies. His foundational education in computer engineering began with his bachelor’s degree from Çanakkale Onsekiz Mart University in 1999, where his thesis examined Slackware Linux operating systems and local area network operations. He graduated from İzmir Selçuk High School, specializing in science. This educational journey has laid the groundwork for his impactful interdisciplinary research.

Professional Experience

Assoc. Prof. Dr. Deniz Mertkan Gezgin has accumulated extensive academic and teaching experience throughout his career in higher education. Since September 13, 2021, he has been serving as an Associate Professor in the Department of Computer Engineering under Article 13b/4. Prior to this, from January 23, 2019, to September 13, 2021, he held the position of Associate Professor at the Faculty of Education. He also served as an Assistant Professor (Yrd. Doç. Dr.) in the same faculty from September 12, 2011, to January 23, 2019. Earlier, from June 15, 2010, to September 6, 2011, he worked as a Lecturer (Öğr. Gör. Dr.) in the Faculty of Education. His academic journey began at the Vocational School of Technical Sciences, where he served as a Lecturer from September 13, 2001, to June 12, 2010. This diverse professional experience highlights his deep commitment to teaching and academic leadership across multiple departments.

Research Focus

Assoc. Prof. Dr. Deniz Mertkan Gezgin’s research primarily focuses on digital technology use and its psychological and educational implications, particularly among students. His work extensively examines smartphone addiction, nomophobia (fear of being without a mobile phone), and the fear of missing out (FoMO), often exploring how these phenomena relate to academic performance, self-regulation, and digital behaviors. He has published studies on the integration of mobile learning, artificial intelligence in education, digital literacy, and cyberpsychology, as well as the use of emerging technologies such as virtual reality in special education. His more recent research includes the use of Explainable Artificial Intelligence (XAI) to predict internet addiction, and the development of psychometric tools like the AIlessphobia scale to assess anxiety related to artificial intelligence in educational settings. Overall, Dr. Gezgin’s work lies at the intersection of educational technology, digital well-being, and behavioral science, making valuable contributions to understanding and improving digital learning environments.

Publication Top Notes

Conclusion

Assoc. Prof. Dr. Deniz Mertkan Gezgin is highly suitable for the Research for Best Researcher Award, owing to his pioneering contributions in cyberpsychology, educational technologies, and AI, backed by a solid publication track, real-world impact, and academic leadership. He bridges technical innovation and human behavior, which aligns strongly with the award’s vision to recognize researchers shaping the future through impactful, interdisciplinary work. With continued global collaboration and practical implementations, his influence is poised to expand further.

Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Assist. Prof. Dr. Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Xiongjun Zhao, Hunan University, China

Assist. Prof. Dr. Xiongjun Zhao is a biomedical AI researcher currently serving as an Assistant Researcher at the School of Information Science and Engineering, Hunan University. He holds a Ph.D. in Computer Science and Technology and a bachelor’s degree in Software Engineering, both from Hunan University, where he was recognized as an Outstanding Graduate and Student Leader. His research focuses on biomedical big data analysis, multi-modal learning, and intelligent medical systems. Dr. Zhao has authored five significant papers, including a CCF-A ACM MM conference paper and three SCI-indexed journal articles. He has also secured four patents in medical AI technologies and leads multiple funded research projects backed by prominent Chinese institutions. An awardee of national honors in mathematics and programming competitions, he brings strong technical expertise in Python, C++, and deep learning frameworks. Dr. Zhao actively mentors students and contributes to international conferences such as IEEE BIBM and ACM MM.

Publication Profile

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Work Experience

Assist. Prof. Dr. Xiongjun Zhao currently holds the position of Assistant Researcher at the School of Information Science and Engineering, Hunan University, a role he began in July 2025. In this capacity, he is deeply involved in cutting-edge research and development focused on biomedical big data analysis, multi-modal learning, and intelligent medical systems. His responsibilities include leading and contributing to a range of scientific research projects, many of which involve national and provincial collaborations. Dr. Zhao plays a pivotal role in advancing academic innovation through his contributions to interdisciplinary projects and by leveraging artificial intelligence in healthcare-related research. In addition to his research activities, he actively mentors students, fostering a dynamic and innovative research environment in the laboratory. His work exemplifies a commitment to technological advancement and academic leadership, positioning him as a key contributor to the development of intelligent medical solutions through data-driven methodologies.

Educational Background

Assist. Prof. Dr. Xiongjun Zhao received his comprehensive academic training from Hunan University, a prestigious institution recognized under China’s Double First-Class Initiative. He pursued a Direct Ph.D. Program in Computer Science and Technology from September 2020 to June 2025, under the mentorship of Prof. Ying Jianguo, a distinguished Changjiang Scholar. His doctoral studies focused on advanced topics in biomedical data science and artificial intelligence. Prior to that, Dr. Zhao completed his Bachelor’s degree in Software Engineering at the same university between September 2016 and June 2020. During his undergraduate years, he was honored as an Outstanding Graduate and recognized for his leadership and community involvement with accolades such as Outstanding Student Leader and Outstanding Volunteer. His solid educational foundation, shaped by rigorous training and guided mentorship, has laid the groundwork for his successful academic and research career in the fields of machine learning and intelligent medical systems.

Research Experience

Assist. Prof. Dr. Xiongjun Zhao has a strong and focused research background in biomedical big data analysis, multi-modal learning, and intelligent medical models. His work integrates advanced machine learning techniques with complex medical datasets to develop deep learning frameworks—particularly Transformer-based models—for clinical diagnostics and decision support systems. His notable academic contributions include the publication of one top-tier conference paper and three SCI-indexed journal articles, along with the successful registration of four patents in the medical AI domain. Dr. Zhao has led and participated in several high-impact research projects, notably those funded by the Hunan Provincial Department of Health and the China Association for Science and Technology. His role as a core researcher in these projects highlights his leadership in interdisciplinary and applied medical research. He has also presented at major international conferences such as ACM MM and IEEE BIBM, reflecting his active engagement in the global scientific community.

Awards and Recognitions

Assist. Prof. Dr. Xiongjun Zhao has received several prestigious awards that reflect his academic excellence and problem-solving capabilities in both theoretical and applied domains. He was awarded the National Second Prize in the National University Student Mathematics Competition (ASC), showcasing his strong analytical and mathematical skills. In competitive programming, he earned the Regional Second Prize in the ACM-ICPC International Collegiate Programming Contest, a globally recognized event for algorithmic proficiency. His team received an Honorable Mention (Top 15%) in the Mathematical Contest in Modeling (MCM/ICPC), further emphasizing his strength in interdisciplinary problem-solving. Additionally, he secured the National Third Prize in the China Graduate Mathematical Modeling Competition and the Provincial Third Prize in the Hunan “Internet+” Innovation and Entrepreneurship Competition. These accolades collectively highlight Dr. Zhao’s commitment to academic rigor, computational excellence, and innovation, and they underscore his readiness to tackle real-world challenges through collaborative and research-driven approaches.

Research Focus

Assist. Prof. Dr. Xiongjun Zhao’s research is primarily centered on biomedical big data analysis, intelligent medical systems, and multi-modal machine learning for clinical decision-making. His work integrates advanced deep learning techniques—such as Transformers, Graph Neural Networks (GNNs), and multimodal prompt learning—into healthcare applications, particularly in medical image analysis, electronic health record (EHR) modeling, and medication guidance systems. His studies include developing continual learning models for EHRs (TransEHR), large language models for drug recommendations (ShennongMGS), and novel diagnostic approaches using multimodal X-ray interpretation and ECG analysis (ECGNN). He also explores multitask and multi-view learning frameworks to enhance predictive accuracy in various clinical settings. His research lies at the intersection of artificial intelligence, medical informatics, and computational biology, aiming to improve diagnostic accuracy, clinical workflow, and personalized medicine. Through high-impact publications and patents, Dr. Zhao contributes significantly to the advancement of AI-driven healthcare technologies and smart medical diagnostics.

Publication Top Notes

  • TransEHR: Alignment-Free Electronic Health Records Continual Learning Across Feature Spaces, Expert Systems with Applications, 2025, DOI: 10.1016/j.eswa.2025.129020

  • ShennongMGS: An LLM-based Chinese Medication Guidance System, ACM Transactions on Management Information Systems, 2025, DOI: 10.1145/3658451

  • Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis, ACM MM (Conference), 2024, DOI: 10.1145/3664647.3681568

  • ECGNN: Enhancing Abnormal Recognition in 12-Lead ECG with Graph Neural Network, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995419

  • UniMed: Multimodal Multitask Learning for Medical Predictions, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995044

  • A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis, IEEE BIBM, 2021, DOI: 10.1109/bibm52615.2021.9669717

  • Multi-View Weighted Feature Fusion Using CNN for Pneumonia Detection on Chest X-Rays, IEEE HEALTHCOM, 2021, DOI: 10.1109/healthcom49281.2021.9399029