Mr. Vangelis Lamprou | Network Intrusion Detection | Best Researcher Award

Vangelis Lamprou at National Technical University of Athens | Greece

Mr. Vaggelis Lamprou is a PhD student in the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA) and a Machine Learning Engineer specializing in deep learning, interpretable AI, and probabilistic modeling. With a strong academic foundation in mathematics and artificial intelligence, he has contributed to European-funded R&D projects in federated learning, generative AI, anomaly detection, and cybersecurity for next-generation networks. His research has been published in leading journals, including Computer Methods and Programs in Biomedicine and the IEEE Open Journal of the Communications Society.

Professional Profile:

Education:

Mr. Vaggelis Lamprou holds a strong academic background spanning mathematics and artificial intelligence, currently pursuing his PhD in the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA) with a focus on deep learning, interpretable AI, and probabilistic modeling. He earned his M.Sc. in Artificial Intelligence from NCSR Demokritos and the University of Piraeus, where his thesis explored the evaluation of deep learning interpretability methods for medical images in terms of faithfulness. Prior to that, he completed an M.Sc. in Mathematics at the University of Bonn, Germany. His academic journey began with a B.Sc. in Mathematics from the National and Kapodistrian University of Athens (NKUA).

Experience:

Mr. Vaggelis Lamprou brings extensive professional expertise in machine learning and data analytics, with a strong track record in both academic and industry-driven innovation. He has been serving as a Machine Learning Engineer at the DSS Lab, EPU-NTUA, where he develops AI-based solutions in federated learning and generative AI for European R&D projects. Previously, as a Machine Learning Engineer at Infili Technologies SA, he designed advanced anomaly detection systems and implemented privacy-preserving mechanisms for federated learning environments. He worked as a Data Analyst at Harbor Lab, where he conducted SQL-based analytics, performed Python-driven exploratory data analysis, and collaborated with the engineering team to build a Port Cost Estimator, optimizing maritime cost assessment processes.

Research Interest:

Mr. Vaggelis Lamprou’s research interests lie at the intersection of artificial intelligence, mathematics, and secure computing, with a focus on advancing both theoretical foundations and practical applications. In AI, he specializes in deep learning architectures, interpretable AI techniques, and probabilistic modeling, aiming to enhance transparency and trust in machine learning systems. His expertise extends to computer vision and natural language processing, particularly in developing interpretability methods for medical imaging and building robust NLP pipelines. He is also engaged in federated learning and cybersecurity research, working on privacy-preserving AI and ensuring trustworthiness in emerging 5G/6G network environments. Additionally, he explores the integration of probability theory and statistical methods into AI, leveraging mathematical rigor to improve model reliability and performance.

Publications Top Noted:

Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey

Year: 2024 | Citations: 16

On the Evaluation of Deep Learning Interpretability Methods for Medical Images Under the Scope of Faithfulness

Year: 2024 | Citations: 4

Grad-CAM vs HiResCAM: A Comparative Study via Quantitative Evaluation Metrics

Year: 2023 | Citations: 4

Conclusion:

With a solid foundation in mathematics, AI, and cybersecurity, Mr. Vangelis Lamprou exemplifies the qualities of a Best Researcher Award recipient in Network Intrusion Detection. His work addresses some of the most pressing challenges in ensuring trust and transparency in next-generation networks. As he continues to expand his research scope and global engagement, he is poised to play a pivotal role in shaping the future of secure AI-driven systems. His combination of academic rigor, technical innovation, and applied impact makes him a deserving candidate for this recognition.

Mr. Abdallah Al-Zubi | Data Science | Best Researcher Award

Abdallah Al-Zubi at University Of Nebraska Lincoln | United States

Mr. Abdallah Alzubi is an accomplished AI engineer and researcher with over eight years of experience in machine learning, data science, and software engineering. Currently pursuing a Ph.D. in AI Engineering at the University of Nebraska-Lincoln, his research focuses on developing MEMS-based analog computing architectures for real-time signal processing, human activity recognition, and structural health monitoring. His contributions span both academic research and industry innovation, including the establishment of the AI department at John Wiley and Sons in Jordan, as well as collaborations on cutting-edge projects funded by the Intelligence Advanced Research Projects Activity (IARPA). He is recognized for bridging theoretical AI research with impactful business and healthcare applications.

Professional Profile:

Google Scholar

Education:

Mr. Abdallah Alzubi is a proficient AI engineer and researcher specializing in data science, machine learning, and software engineering, with extensive academic and professional experience. He is currently pursuing a Ph.D. in AI Engineering at the University of Nebraska-Lincoln, USA, focusing on MEMS-based Analog Computing. He also holds an M.S. in AI Engineering from the same institution, where he completed his thesis on Gradient-Based Multi-Time-Scale Trainable Continuous Time Recurrent Networks, as well as an M.S. in Data Science from Princess Sumaya University for Technology, Jordan, with research on Pathfinder Optimization clustering techniques. His academic journey began with a B.S. in Computer Engineering from Jordan University of Science & Technology, where he developed an automated Arabic optical character recognition system.

Experience:

Mr. Alzubi serves as a Research Assistant at the University of Nebraska-Lincoln, where he develops MEMS-based hardware simulations for structural health monitoring and signal denoising using TensorFlow and Keras, while also designing AI models for seismic structural assessments and human activity detection. Previously, as an AI Engineer at John Wiley & Sons (NJ), he pioneered the establishment of their AI Department in Jordan, enhancing speech recognition systems, building big data-driven article recommendation engines, and improving sentiment analysis accuracy. Earlier in his career, he worked as a Software Engineer at Globitel, Jordan, where he created mobile proximity matching services for taxi dispatching and developed secure authentication solutions (Mobile Connect) for telecom clients. As a Solution Developer at ILS Saudi Co. Ltd, he implemented ERP systems to optimize operations across manufacturing, HR, and finance. At SEDCO, Jordan, he further contributed by enhancing customer queuing management systems—reducing communication latency sevenfold—and integrating smart advertising and multilingual functionalities.

Research Interest:

His research interests span across MEMS-based analog computing for low-power AI applications, machine learning for structural health monitoring and earthquake response, human activity recognition in healthcare, natural language processing for speech recognition and sentiment analysis, and big data analytics for real-time AI system design.

Publications Top Noted:

Automated System for Arabic Optical Character Recognition with Lookup Dictionary
Year: 2012
Citations: 21
Automated System for Arabic Optical Character Recognition
Year: 2012
Citations: 9

G-CTRNN: A Trainable Low-Power Continuous-Time Neural Network for Human Activity Recognition in Healthcare Applications
Year: 2025
A Novel MEMS Reservoir Computing Approach for Classifying Human Acceleration Activity Signal
Year: 2025
Distributed and Automated Machine Learning in Big Data Stream Analytics
Year: 2019
Citations: 1

Conclusion:

Mr. Abdallah Al-Zubi exemplifies the qualities of a forward-thinking researcher in AI and Data Science. His innovative work on MEMS-based analog computing, coupled with contributions to structural health monitoring, human activity recognition, and big data-driven AI, positions him as a global leader in next-generation artificial intelligence research. His unique blend of academic rigor, industry leadership, and impactful real-world applications makes him a highly deserving candidate for the Best Researcher Award. With his ongoing contributions, he is poised to play a critical role in shaping the future of low-power AI systems and intelligent infrastructure solutions.

Abdallah Al-Zubi | Data Science | Best Researcher Award