Angelos Athanasiadis | Neural Networks | Research Excellence

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Mr. Angelos Athanasiadis | Neural Networks | Research Excellence

Aristotle University of Thessaloniki | Greece

Angelos Athanasiadis is a Ph.D. candidate in Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH), specializing in FPGA-based acceleration of Convolutional Neural Networks. With expertise spanning embedded system development, heterogeneous computing, and cyber-physical systems, he has contributed to both academic and industrial innovation through participation in EU research initiatives—including the ADVISER and REDESIGN projects—and through consultancy and R&D roles at EXAPSYS and SEEMS PC. His work focuses on advancing energy-efficient hardware acceleration, leading to the development of a parameterizable HLS matrix multiplication library for AMD FPGAs that enables full-precision CNN inference for accuracy-critical domains such as aerial monitoring and autonomous embedded systems. He further expanded the field with FUSION, an open-source high-fidelity distributed emulation framework integrating QEMU with OMNeT++ via HLA/CERTI synchronization to support deterministic, timing-aware multi-node execution and realistic prototyping of heterogeneous systems. Complementing his strong technical background, he holds an MBA awarded with high distinction and an M.Eng. in electronics and computer systems, supported by internships at Cadence Design Systems in Munich.

Profiles: Orcid | Google Scholar

Featured Publications

"An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs", A Athanasiadis, N Tampouratzis, I Papaefstathiou, Integration, 2025.

"Energy-Efficient FPGA Framework for Non-Quantized Convolutional Neural Networks", A Athanasiadis, N Tampouratzis, I Papaefstathiou, arXiv preprint arXiv:2510.13362, 2024.

"An Open-source HLS Fully Parameterizable Matrix Multiplication Library for AMD FPGAs", A Athanasiadis, N Tampouratzis, I Papaefstathiou, WiPiEC Journal-Works in Progress in Embedded Computing Journal 10 (2), 2024.

Farhad Hossain Sojib | Data Science | Best Researcher Award

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Mr. Farhad Hossain Sojib | Data Science | Best Researcher Award 

University of Hull | Bangladesh

Mr. Farhad Hossain Sojib is an engineer with a strong foundation in electronics and communication engineering and a growing specialization in data science and artificial intelligence. He is currently pursuing his M.Sc. in Artificial Intelligence and Data Science at the University of Hull, United Kingdom, following the completion of his B.Sc. in Engineering from Hajee Mohammad Danesh Science and Technology University, Bangladesh, where he conducted notable research on explainable AI in educational data mining and machine learning applications in 5G antenna optimization. With professional experience as an IELTS Instructor at Lexicon Plus, he has trained over 50 students, developed course materials, and mentored junior instructors. His leadership and organizational skills were further demonstrated through his role as a Program Committee Member at the IEEE Student Branch, HSTU, where he managed events, seminars, and competitions. Additionally, his internship at BRACNet Limited provided hands-on experience in ISP operations, ICT technologies, and server management. Farhad combines his technical expertise, research acumen, and collaborative mindset to contribute meaningfully to the fields of machine learning and data-driven innovation.

Profiles: Orcid 

Featured Publications

"The integration of explainable AI in Educational Data Mining for student academic performance prediction and support systems", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman, Telematics and Informatics Reports, 2025.

"A Bioinformatics Approach to Uncover Hub Genes and Potential Drug Targets of Stroke, Heart-Disease, Hyperglycemia, and Hypertension", Md. Emran Biswas; M D. Fazle Hasan Mihad; Farhad Hossain Sojib; Mohammad Jubair Ahmmed; M D Galib Hasan; Md. Jobare Hossain; Md. Abul Basar; Md. Mehedi Islam; Md. Delowar Hossain; Md. Selim Hossain et al., 27th International Conference on Computer and Information Technology (ICCIT), 2024.

"An Explainable Educational Data Mining System for Predicting Student Academic Performance", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman; FARHAD HOSSAIN SOJIB, 2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, 2024.

Faisal Alshami | Machine Learning | Best Researcher Award

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Faisal Alshami | Machine Learning | Best Researcher Award

Dalian University of Technology | China

Author Profile

Google Scholar

Early Academic Pursuits

Faisal Alshami’s academic journey reflects a deep commitment to software engineering and technological innovation. He began his undergraduate studies at Sana’a University, Yemen, earning a BSc in Network Technology and Computer Security (2008–2012). His undergraduate thesis, “General Management System for Plant Protection,” showcased his early ability to integrate security and system management using ASP.NET, C#, and VPN with OSPF protocols, signaling his strong foundation in both networking and software development. Building on this groundwork, Faisal pursued a Master’s in Software Engineering at Northeastern University, China (2019–2022), where he specialized in advanced machine learning techniques. His master’s thesis, “Design and Implementation of Web API Recommendation System Based on Deep Learning,” utilized CNN, BLSTM, K-modes, and Word2Vec, demonstrating his growing expertise in AI-driven software solutions. Currently, Faisal is advancing his academic pursuits with a PhD in Software Engineering at Dalian University of Technology, China, focusing on federated learning, distributed systems, blockchain, edge computing, and graph neural networks (GNNs).

Professional Endeavors

Alongside his academic progression, Faisal has accumulated over 5 years of professional experience in the software and networking industry. His early career as a VoIP Engineer/Developer at Communication Services Company (2013–2015) allowed him to develop communication APIs and optimize large-scale systems. As a Network Manager and Systems Engineer at EliteTecs (2015–2016), he designed high-reliability networks using advanced protocols such as OSPF, EIGRP, and WiMAX, showcasing his expertise in secure and resilient infrastructures. His role as Full-Stack Developer and DevOps Lead at Almorisi Exchange Company (2016–2018) highlighted his ability to manage mission-critical systems with real-time performance and security. Here, Faisal excelled in building scalable architectures, simulation frameworks, and automated DevOps pipelines, which contributed to operational excellence.

Contributions and Research Focus

Faisal’s research is strategically positioned at the intersection of distributed systems, intelligent computing, and aerospace applications. His focus includes:

  • Federated learning and secure communication for multi-agent systems such as satellite constellations.

  • Edge computing and real-time distributed systems tailored for resource-constrained environments.

  • Robust machine learning frameworks for aerospace, automation, and high-reliability embedded systems.

  • Blockchain integration with AI to enhance security in data networks.

  • Simulation and testing methodologies to ensure fault tolerance in mission-critical software.
    This body of research reflects his ambition to address pressing challenges in space exploration, aerospace engineering, and advanced communication networks.

Impact and Influence

Faisal’s impact lies in bridging the gap between theory and applied innovation. His academic research is not confined to publications alone but extends into real-world applications in secure communications, high-availability systems, and intelligent software architectures. By combining his professional experience with cutting-edge research, Faisal has influenced the fields of network security, distributed computing, and AI-driven system optimization, making his contributions valuable to both academia and industry.

Academic Cites

His work has strong potential for academic citations due to its interdisciplinary nature—linking software engineering, AI, networking, and aerospace technologies. His focus on federated learning, blockchain, and edge computing positions his research at the forefront of emerging scholarly and industrial discussions, ensuring that his publications will attract citations in journals focusing on AI, distributed systems, cybersecurity, and aerospace software engineering.

Legacy and Future Contributions

Faisal Alshami is on a trajectory to build a lasting legacy in intelligent, secure, and scalable software engineering systems. His research is particularly impactful in aerospace applications and secure communications, areas that are becoming increasingly vital in a digital and space-driven era. As he progresses with his doctoral research, Faisal is expected to contribute significantly to the development of resilient federated learning frameworks, advanced distributed architectures, and mission-critical simulations. His blend of academic depth and industry experience ensures that his future work will leave a lasting influence on next-generation computing systems and aerospace engineering technologies.

Other Notable Highlights

  • Certifications: Faisal holds multiple certifications, including Neural Networks & Deep Learning (DeepLearning.AI), CCNP, CCNA, and advanced language certifications (Chinese HSK4, English YALI).

  • Training: He gained practical exposure at NEUSOFT Project Training, where he contributed to developing the Borrow-Seller System (BSS) using Java, Spring Boot, Vue.js, and Android Studio.

  • Core Competencies: His expertise spans software architecture, DevOps, distributed systems, full-stack development, secure networking, and agile collaboration.

Conclusion

In conclusion, Faisal Alshami is an emerging leader in the domain of software engineering, distributed systems, and intelligent computing. His academic journey, professional experiences, and research pursuits demonstrate a rare combination of technical mastery, innovation, and practical problem-solving skills. With his ongoing doctoral work and focus on future technologies such as federated learning, blockchain, and aerospace applications, Faisal is poised to make significant contributions that will influence both academia and industry for years to come.

Notable Publications

"A detailed analysis of benchmark datasets for network intrusion detection system" 

  • Author: M Ghurab, G Gaphari, F Alshami, R Alshamy, S Othman
  • Journal: Asian Journal of Research in Computer Science
  • Year: 2021

"Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm" 

  • Author: R Alshamy, M Ghurab, S Othman, F Alshami
  • Journal: International Conference on Advances in Cyber Security
  • Year: 2021