Assist. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Researcher | Samsun University | Turkey

Assist. Prof. Dr. Ozgur Tonkal is a distinguished academic and researcher currently serving as an Assistant Professor in the Department of Software Engineering at Samsun University, Türkiye. With an extensive background in Computer Engineering, Cybersecurity, and Software Defined Networks (SDN), he has established a strong academic and professional presence in the rapidly evolving field of information and communication technologies. Dr. Tonkal earned his Doctor of Philosophy (Ph.D.) degree in Computer Engineering from Gazi University, where he successfully completed his doctoral thesis titled “Autonomous Attack Detection and Mitigation Model by Network Traffic Aware Approach in Software Defined Networks,” which demonstrated innovative solutions for traffic-aware autonomous threat detection systems in SDN environments. He also holds a Master of Science in Computer Science from Gazi University and multiple Bachelor’s degrees from Gazi University, Karabuk University, and Anadolu University, combining expertise in computer systems education, computer engineering, and business administration. Throughout his career, Assist. Prof. Dr. Ozgur Tonkal has been recognized for his outstanding teaching, administrative leadership, and technical proficiency in cybersecurity, artificial intelligence, IoT, and computer network design. As a core faculty member, he teaches courses on Cybersecurity, IoT, Big Data, Artificial Intelligence, Computer Networks, and Web Programming while also serving as the Vice President of the Software Engineering Department, Technical Advisor to the Cybersecurity Student Community, and Manager of the University Cyber Incident Response Team. He has authored 3 documents, received 100 Citations, and holds an h-index of 2, reflecting his growing influence and scholarly impact in the field. His major research interests include Software Defined Networking (SDN), Machine Learning, Computer Networks, Cybersecurity, Big Data, and Network Security Automation. His technical expertise extends to programming in Python, MATLAB, and SQL, network system design and risk analysis, virtualization systems (Hyper-V, VMware), and machine learning applications for intrusion detection. He possesses multiple professional certifications from global institutions, including Cisco (CCNAv7, Network Security, IoT, and CyberOps Associate), Oracle (Database Design and SQL Programming), Exemplar Global (ISO/IEC 27001 ISMS Lead Auditor), and Google (Machine Learning Crash Course). His participation in the COST Action CA22168 project and contribution to international symposiums and conferences illustrate his active engagement with global research communities. In addition to his research and teaching responsibilities, he has taken on administrative roles as Acting Head of the IT Department at Samsun University and Technical Advisor for international robotics competitions, demonstrating his leadership and commitment to advancing education and innovation. Assist. Prof. Dr. Ozgur Tonkal’s scholarly works have been published in reputable journals indexed in Scopus and IEEE, with notable publications in International Journal of Imaging Systems and Technology, Electronics, and Gazi University Journal of Science Part C: Design and Technology, among others.

Profile:  Google scholar | Scopus | ORCID

Featured Publications

  1. Sertkaya, M. E., Ergen, B., Türkoğlu, M., & Tonkal, Ö. (2024). Accurate diagnosis of dementia and Alzheimer’s with deep network approach based on multi-channel feature extraction and selection. International Journal of Imaging Systems and Technology, 34. [Citations: 25]

  2. Tonkal, Ö., Polat, H., Başaran, E., Cömert, Z., & Kocaoğlu, R. (2021). Machine learning approach equipped with neighbourhood component analysis for DDoS attack detection in software-defined networking. Electronics, 10. [Citations: 40]

  3. Tonkal, Ö., Polat, H. (2021). Traffic classification and comparative analysis with machine learning algorithms in software defined networks. Gazi University Journal of Science Part C: Design and Technology, 9(1), 71–83. [Citations: 20]

  4. Tonkal, Ö. (2024). Cyber threat analytics in data science: Intrusion detection and prevention systems. In Current Studies in Data Science and Analytics. ISRES Publishing. [Citations: 10]

  5. Mercimek, T., & Tonkal, Ö. (2024). Social media criminals. In Proceedings of the 7th International Antalya Scientific Research and Innovative Studies Congress. [Citations: 5]

 

Jana Al Haj Ali | Computer Science and Artificial Intelligence | Best Researcher Award

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Mrs. Jana Al Haj Ali | Computer Science and Artificial Intelligence | Best Researcher Award

Mrs. Jana Al Haj Ali | Computer Science and Artificial Intelligence | PhD Student | University of Lorraine | France

Mrs. Jana Al Haj Ali is an accomplished researcher and PhD student in Computer Engineering, specializing in the design and implementation of cognitive digital twins for industrial applications. Her work integrates neuro-symbolic AI approaches to enable intelligent, adaptive, and human-centric human-robot interaction within cyber-physical systems. Through her innovative research, she has contributed to advancing the understanding of cognitive architectures, simulation models, and interoperability protocols, aiming to improve automation, safety, and efficiency in Industry 5.0 contexts. She is known for combining technical expertise, scientific rigor, and collaborative spirit to drive impactful solutions at the intersection of artificial intelligence, robotics, and cognitive systems.

Professional Profile 

Education

Mrs. Jana Al Haj Ali holds a Bachelor’s degree in Mathematics, followed by a Master’s degree in Mathematical Engineering for Data Science, which provided her with an interdisciplinary foundation in mathematical modeling, machine learning, and computational techniques. She is currently pursuing her doctoral studies in Computer Engineering at a leading research institute in France, where she is actively engaged in high-impact research focusing on cognitive digital twin technologies. Her educational background bridges mathematics, data science, and computer engineering, allowing her to approach complex research problems from both theoretical and applied perspectives.

Experience

Mrs. Jana Al Haj Ali has extensive research experience in the development of modular architectures for cognitive digital twins, focusing on emulation, cognition, and simulation functionalities. She has implemented cognitive exchange protocols between industrial robots and human operators, enabling adaptive reconfiguration of cyber-physical systems based on real-time cognitive feedback. She also completed a visiting research project at a prominent European research institute, where she designed cognitive models and integrated them into simulation environments to evaluate collaborative performance. Additionally, she has experience in data analysis, machine learning modeling, and physical risk estimation from her earlier research internships.

Research Interest

Her primary research interests include cognitive cyber-physical systems, cognitive digital twins, neuro-symbolic AI, knowledge representation, and human-robot collaboration. She is particularly focused on enhancing cognitive interoperability, developing architectures that combine deep learning with symbolic reasoning, and designing intelligent simulation frameworks that predict system behavior in real-time. Her work aims to address key challenges in Industry 5.0 by creating more resilient, adaptive, and human-centric automation solutions.

Award

Mrs. Jana Al Haj Ali has been recognized for her contributions through opportunities to present her research at prestigious international conferences, summer schools, and national symposia. Her participation in scientific events and collaboration with international research teams reflects her growing impact in the academic community. She is highly regarded for her ability to translate complex cognitive models into practical implementations, earning acknowledgment from peers and mentors for her innovative approach.

Selected Publication

  • Human Digital Twins: A Systematic Literature Review and Concept Disambiguation for Industry 5.0 (2025) – 45 citations

  • Cognition in Digital Twins for Cyber-Physical Systems and Humans: Where and Why? (2024) – 30 citations

  • Cognitive Architecture for Cognitive Cyber-Physical Systems (2024) – 28 citations

  • Cognitive Systems and Interoperability in the Enterprise: A Systematic Literature Review (2024) – 33 citations

Conclusion

Mrs. Jana Al Haj Ali is an outstanding candidate for this award, with a strong academic background, impactful research contributions, and a commitment to advancing the field of cognitive digital twins and human-robot collaboration. Her work demonstrates a unique combination of theoretical innovation and practical application, contributing to the future of intelligent and adaptive industrial systems. With a growing publication record, active participation in international collaborations, and dedication to knowledge dissemination, she is well positioned to emerge as a leader in cognitive cyber-physical systems research.

 

William Lawless | Computer Science and Artificial Intelligence | Best Researcher Award

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William Lawless | Computer Science and Artificial Intelligence | Best Researcher Award

Dr William Lawless, Paine College, United States

W.F. Lawless is a pioneering mechanical engineer known for blowing the whistle on nuclear waste mismanagement in 1983. He earned his PhD in 1992, focusing on organizational failures among leading scientists. Invited to join the DOE’s citizens advisory board at Savannah River Site, he coauthored key recommendations for environmental remediation. His research centers on autonomous human-machine teams, and he has edited nine influential books on AI, including the award-nominated Human-Machine Shared Contexts. With over 300 peer-reviewed publications, he has organized multiple symposia and special issues, contributing significantly to the field of artificial intelligence. 🔬🤖📚

Publication profile

Orcid

Research focus

William Lawless’s research focuses on the dynamics of human-machine collaboration, particularly in the context of autonomy and uncertainty. His work explores how knowledge, risk perception, and interdependence influence the effectiveness of autonomous teams. By examining models that integrate quantum-like principles, he aims to enhance our understanding of decision-making processes within complex systems. His publications highlight the essential tension between knowledge and uncertainty, proposing new frameworks for improving human-machine interactions. This interdisciplinary approach bridges technology and human factors, contributing significantly to fields like robotics, artificial intelligence, and human-computer interaction. 🤖📊🔍

Publication top notes

Shannon Holes, Black Holes, and Knowledge: The Essential Tension for Autonomous Human–Machine Teams Facing Uncertainty

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

Risk Determination versus Risk Perception: A New Model of Reality for Human–Machine Autonomy