Fengrui Hao | Computer Science | Best Researcher Award

Published on by Academic Excellence

Dr. Fengrui Hao | Computer Science | Best Researcher Award

Jinan University, China

Dr. Fengrui Hao is an emerging researcher in the field of computer science, currently pursuing his Ph.D. in Cyber Security at the School of Information Science and Technology, Jinan University, Guangzhou, China. He holds a B.S. degree in Information Management and Information Systems and an M.S. degree in Computer Technology from Guilin University of Electronic Technology, which laid the foundation for his deep engagement with advanced computing and security research. His primary focus lies in adversarial machine learning and trustworthy artificial intelligence, where he has made significant contributions to strengthening AI systems against vulnerabilities and ensuring fairness, transparency, and robustness in their applications. With more than ten publications in prestigious journals and conferences such as IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Industrial Informatics (TII), and IEEE Transactions on Knowledge and Data Engineering (TKDE), Dr. Hao has established himself as a promising scholar. His research contributions include the development of novel attack and defense mechanisms, fairness-aware algorithms, and privacy-preserving techniques for graph data publishing, all of which are shaping the foundation of secure and ethical AI. His work has been recognized through two completed or ongoing research projects, one consultancy project, and an impressive record of sixteen patents under process. With a citation index of twenty, his influence in the field continues to expand as he pursues innovative research directions. Dr. Hao’s dedication to advancing adversarial learning and trustworthy AI reflects his vision of creating resilient, secure, and human-centered AI technologies for the future.

Profiles: Scopus | Orcid

Featured Publications

  • FBA: Fairness Backdoor Attack on Graph Neural Networks – IEEE Transactions on Dependable and Secure Computing, 2025, DOI: 10.1109/TDSC.2025.3563668

  • dK-DGDP: A Differential Privacy Approach on Directed Social Network Graphs – Computers & Security, 2025, DOI: 10.1016/j.cose.2025.104647

  • GCPA: GAN-Based Collusive Poisoning Attack in Federated Recommender Systems – IEEE Transactions on Knowledge and Data Engineering, 2025, DOI: 10.1109/TKDE.2025.3579807

  • CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models – ACM Transactions on Privacy and Security, 2024, DOI: 10.1145/3678007

  • Collusive Model Poisoning Attack in Decentralized Federated Learning – IEEE Transactions on Industrial Informatics, 2024, DOI: 10.1109/TII.2023.3342901

 

Vincenzo Arceri | Computer Science | Best Researcher Award

Published on by Academic Excellence

Dr. Vincenzo Arceri | Computer Science | Best Researcher Award

Dr. Vincenzo Arceri | Computer Science | University of Parma | Italy

Dr. Vincenzo Arceri is an accomplished computer scientist and Assistant Professor at the University of Parma, Italy. His expertise lies in abstract interpretation, static program analysis, blockchain security, and formal methods for ensuring software reliability. With a strong academic background and multiple research fellowships, he has established himself as a leading voice in advancing secure, dependable software systems. Dr. Arceri has contributed significantly to the development of static analysis tools, including LiSA, a generic library for static analysis, and EVMLiSA, a specialized analyzer for Ethereum smart contracts. His research extends into improving the quality and security of large language model–generated code, showcasing his commitment to addressing emerging challenges in artificial intelligence and blockchain domains. Recognized internationally through invitations to prestigious Dagstuhl Seminars, awards, and collaborations, Dr. Arceri combines research excellence with impactful teaching, mentoring students in programming and fostering the next generation of computer scientists.

Author Profiles

Orcid | Google Scholar

Education

Dr. Vincenzo Arceri pursued his academic journey at the University of Verona, Italy, where he obtained a Bachelor’s Degree in Computer Science in 2014 with a thesis on semantic analysis techniques for JavaScript. He continued his studies with a Master’s Degree in Computer Science, graduating cum laude in 2016, with a thesis focusing on static type analysis for PHP. Building upon his foundation, he earned his Ph.D. in Computer Science in 2020, presenting a dissertation titled “Taming Strings in Dynamic Languages – An Abstract Interpretation-based Static Analysis Approach.” His doctoral work, supervised by Prof. Isabella Mastroeni, was critically acclaimed by international reviewers such as Prof. Sergio Maffeis and Prof. Xavier Rival. Through this academic pathway, Dr. Arceri specialized in the rigorous application of abstract interpretation to real-world programming challenges, setting the stage for his future contributions to static analysis, software verification, and blockchain-related applications.

Experience

Dr. Vincenzo Arceri began his research career as a Postdoctoral Researcher at Ca’ Foscari University of Venice (2019–2021), where he worked on IoT applications in smart cities and the development of static analysis tools for Go, particularly in the context of blockchain smart contracts. His research there focused on formal verification and the precision–efficiency trade-offs in string analysis. In September 2021, he joined the University of Parma as an Assistant Professor, where he currently teaches Fundamentals of Programming to undergraduate students while continuing his research in advanced program analysis. His contributions include designing LiSA, a generic static analysis framework, and EVMLiSA, a static analyzer for Ethereum Virtual Machine bytecode. He has also explored static analysis for unsafe Rust programs and LLM-generated code. Dr. Arceri’s professional trajectory reflects a balance of teaching, applied research, and international collaboration with academic and industry partners.

Awards and Honors

Dr. Vincenzo Arceri’s research excellence has been recognized through prestigious awards and honors. In 2019, he received the Best Paper Award at VALID 2019 for his contribution to the operational semantics of Solidity, highlighting his innovative work in blockchain verification. His international reputation was further affirmed with scholarships such as the Marktoberdorf Summer School in 2018, which focused on engineering secure and dependable software systems. In 2023, he was awarded INdAM GNCS funding to support his participation in international conferences, workshops, and seminars. Furthermore, Dr. Arceri has been invited to the distinguished Dagstuhl Seminars in 2023 and 2025, gatherings known for shaping the future of computer science research. These invitations underscore his standing as an expert in abstract interpretation and static analysis. Collectively, these accolades reflect his academic rigor, groundbreaking contributions, and the international recognition he has garnered for advancing software reliability and security.

Research Focus

Dr. Vincenzo Arceri’s research centers on the application of abstract interpretation to improve the security, reliability, and correctness of software systems. He has dedicated his career to advancing static program analysis for a wide range of programming paradigms, from dynamic languages such as JavaScript and PHP to domain-specific blockchain applications. His work also addresses the challenges of analyzing unsafe Rust code and verifying smart contracts in Go and Ethereum. Notably, he has developed LiSA, a multilanguage static analysis framework, and EVMLiSA, a static analyzer tailored to EVM bytecode, demonstrating his ability to merge theoretical rigor with practical implementations. His recent projects explore the safety of LLM-generated code, aiming to ensure that AI-driven programming integrates robust security principles. By balancing precision and performance in static analysis, Dr. Arceri’s work provides a critical foundation for future-proof software engineering, cross-blockchain applications, and secure AI-integrated development practices.

Publications

  • Static analysis for dummies: experiencing LiSA.

  • Analyzing Dynamic Code: A Sound Abstract Interpreter for Evil Eval.

  • LiSA: a generic framework for multilanguage static analysis.

  • Static Program Analysis for String Manipulation Languages.

  • Static analysis for ECMAScript string manipulation programs.

  • Ensuring determinism in blockchain software with GoLiSA: an industrial experience report.

  • Information flow analysis for detecting non-determinism in blockchain.

  • Twinning automata and regular expressions for string static analysis.

  • Abstract domains for type juggling.

  • Relational string abstract domains.

Conclusion

Dr. Vincenzo Arceri exemplifies the qualities of a modern computer scientist—innovative, collaborative, and deeply committed to advancing the reliability of digital systems. His work bridges theory and practice, from foundational contributions in abstract interpretation to impactful tools for blockchain verification and AI-generated code analysis. With a growing body of influential publications, awards, and teaching contributions, he stands as a leading researcher shaping the future of secure and dependable software systems.

 

NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

Published on by Academic Excellence

Mr. NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

👤 Mr. NIKOLAOS EPISKOPOS, IBM, Greece

Nikolaos Episkopos is an accomplished Data Scientist, Software Developer, and Data Science Consultant with over eight years of professional experience. Based in Athens, Greece, Nikolaos specializes in AI, cybersecurity, and predictive medicine, contributing to impactful EU-funded R&D projects and Open Source Software initiatives. His innovative work includes AI solutions for fraud detection, federated learning toolkits for intrusion detection, and optimizing data pipelines. Passionate about the intersection of technological advancements and societal impact, Nikolaos has played pivotal roles in enhancing banking services, securing SCADA systems, and developing blockchain-based video streaming systems. As a professional with a robust academic background and diverse technical skills, he combines creativity and precision to deliver groundbreaking solutions.

Professional Profile

Orcid

🌟 Evaluation of Nikolaos Episkopos for the Research for Best Researcher Award

Summary of Suitability

Nikolaos Episkopos demonstrates an exceptional profile as a data scientist and software developer with significant contributions to research and development projects, particularly in the domains of artificial intelligence (AI), cybersecurity, and healthcare. With over eight years of professional experience, he has effectively bridged academia and industry, showcasing the ability to lead and innovate in complex technological domains. His work has resulted in tangible outcomes, including funding acquisitions, novel AI solutions, and impactful publications.

Nikolaos’s recent achievements at IBM and INLECOM highlight his ability to tackle real-world challenges through data-driven innovation. At IBM, he developed advanced AI models for fraud detection, contributing to financial security solutions. His role at INLECOM involved technical project management and significant contributions to EU Horizon projects, where his efforts secured funding and established strategic partnerships. These accomplishments reflect his leadership, technical expertise, and collaborative skills in advancing scientific research.

🎓 Education 

Nikolaos holds a Master’s degree in Data Science & Information Technologies from the National and Kapodistrian University of Athens, where he honed expertise in AI, data analysis, and cybersecurity. Currently pursuing an MSc in Cybersecurity at the University of West Attica, he is expanding his knowledge of threat modeling and advanced cryptographic techniques. His academic journey reflects a commitment to excellence, as he consistently excelled in designing AI models and deploying secure, scalable systems. Nikolaos’ education equips him to navigate the rapidly evolving landscape of AI and cybersecurity, combining rigorous academic training with practical, hands-on experience to solve complex technical challenges effectively.

💼  Professional Experience

Nikolaos Episkopos has held roles at leading organizations like IBM, INLECOM, and MetaMind Innovations. At IBM, he spearheaded the development of AI solutions for card fraud detection and enhanced banking services. At INLECOM, he managed Horizon projects, securing significant funding and partnerships while optimizing algorithms for AI tools. During his tenure at MetaMind Innovations, he developed Federated Learning toolkits for intrusion detection, authored academic papers, and secured SCADA systems. At Fogus Innovations, Nikolaos implemented blockchain-enabled video streaming optimization and co-authored publications on advanced AI platforms. His ability to lead technical projects, develop AI models, and foster innovation underscores his exceptional contribution to the tech industry.

🏅 Awards and Recognition 

Nikolaos has been recognized for his contributions to EU-funded Horizon projects, which have brought substantial funding and technological advancements to his organizations. He co-authored papers published in prestigious journals like IEEE Transactions on Mobile Computing and Computer Science Review, highlighting his expertise in AI and cybersecurity. Additionally, his innovative solutions in fraud detection and SCADA security have been acknowledged within the tech community. Nikolaos’ commitment to open-source projects on GitHub further demonstrates his dedication to knowledge sharing and continuous improvement. His achievements reflect a career driven by excellence and societal impact.

🌍 Research Skills On Computer Science

Nikolaos excels in designing and deploying AI-driven solutions across domains such as cybersecurity, predictive medicine, and fraud detection. His expertise encompasses Federated Learning, blockchain integration, and data analysis using tools like TensorFlow, PyTorch, and Spark. Skilled in optimizing algorithms and building scalable data pipelines, Nikolaos has delivered solutions that reduce execution time and enhance efficiency. His academic research, coupled with industry application, positions him as a thought leader in leveraging AI for societal impact.

📖 Publication Top Notes

1. A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions
  • Author(s): Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, et al.
  • Citation: Computer Science Review, 2025-05
2. To DASH, or Not to DASH? Optimal Video Bitrate Selection and Edge Network Caching in MEC-Empowered Slice-Enabled Networks
  • Author(s): Dionysis Xenakis, Nikolaos Episkopos
  • Citation: IEEE Transactions on Vehicular Technology, 2024-04
3. PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2022-10-24
4. Cache-Aware Adaptive Video Streaming in 5G networks
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2021-05-04
5. SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions
  • Author(s): Aristeidis Farao, Sakshyam Panda, Sofia Anna Menesidou, Entso Veliou, Nikolaos Episkopos, George Kalatzantonakis, Farnaz Mohammadi, Nikolaos Georgopoulos, Michael Sirivianos, Nikos Salamanos, et al.
  • Citation: Trust, Privacy and Security in Digital Business (TrustBus), 2020-09-14
6. On-device caching of popular video content on Android-powered devices
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2018-08-14