Mr. Gabriel Amaizu | Charismatic Leadership | Best Researcher Award

Towson University | United States

Gabriel C. Amaizu is an AI Engineer, researcher, and educator with over seven years of professional experience spanning artificial intelligence, machine learning, computer vision, cybersecurity, and digital twin technologies. Currently a Ph.D. candidate in Information Technology at Towson University, he also contributes as an Adjunct Faculty and AI Research Engineer in the Digital Twins Lab, where he develops explainable and blockchain-secured AI systems for healthcare and critical infrastructure. He holds an M.Sc. in IT Convergence Engineering from KIT, South Korea, and a B.Sc. in Computer Applications from Bangalore University. Gabriel has previously worked as an AI Engineer at the ICT Convergence Research Center and as a Graduate Researcher at KIT, producing impactful work in federated learning, network security, 3D-printer anomaly detection, and RF-based anti-drone systems. His industry collaborations include projects with LGU+, Conception 3D, and multiple research labs. With 20+ peer-reviewed publications, 600+ citations, and an H-index of 8, his research interests focus on digital twins, blockchain-enabled AI, distributed learning, medical imaging, and secure IoT systems. Recognized as an IEEE Senior Member, Gabriel continues to advance trustworthy, scalable AI solutions that bridge academic innovation with real-world applications.

Profile: Google Scholar

Featured Publications

Njoku, J. N., Nwakanma, C. I., Amaizu, G. C., & Kim, D. S. (2023).
Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems. IET Intelligent Transport Systems, 17(1), 1–21.

Amaizu, G. C., Nwakanma, C. I., Bhardwaj, S., Lee, J. M., & Kim, D. S. (2021).
Composite and efficient DDoS attack detection framework for B5G networks. Computer Networks, 188, Article 107871.

Amaizu, G. C., Nwakanma, C. I., Lee, J. M., & Kim, D. S. (2020).
Investigating network intrusion detection datasets using machine learning. In Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE.
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Sampedro, G. A. R., Agron, D. J. S., Amaizu, G. C., Kim, D. S., & Lee, J. M. (2022).
Design of an in-process quality monitoring strategy for FDM-type 3D printer using deep learning. Applied Sciences, 12(17), Article 8753.

Amaizu, G. C., Njoku, J. N., Lee, J. M., & Kim, D. S. (2024).
Metaverse in advanced manufacturing: Background, applications, limitations, open issues & future directions. ICT Express, 10(2), 233–255.

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