Chung-Horng Lung | Engineering | Best Researcher Award

Full Professor at Carleton University, Canada

Dr. Chung-Horng Lung is a distinguished professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa. With a career spanning over three decades in academia and industry, he has made significant contributions to software engineering, network security, and artificial intelligence. Recognized as one of the world’s top 2% most-cited researchers (Stanford-Elsevier, 2022 & 2023), his work has influenced various domains, including machine learning-based security systems, intelligent data processing, and network optimization. Prior to joining Carleton University, he held senior engineering positions at Nortel Networks, where he worked on software architecture, network traffic engineering, and MPLS-based communication technologies. His extensive research, mentorship, and interdisciplinary collaborations have earned him a reputation as a leading scholar in his field. Alongside his academic contributions, Dr. Lung is also a Professional Engineer (P.Eng.) in Ontario, further validating his expertise and impact in the engineering community.

Professional Profile

• Scopus Profile
• Google Scholar

Education

Dr. Lung holds a Ph.D. in Computer Science and Engineering from Arizona State University, Tempe, earned in 1994. His journey in academia began with a Master’s degree in Computer Science and Engineering from the same institution in 1988, following a Bachelor’s degree in Computer Science and Engineering from Chung-Yuan Christian University, Taiwan, in 1982. His academic background provided him with a strong foundation in software engineering, network security, and intelligent computing. During his doctoral studies, he worked extensively on distributed systems and software engineering methodologies, laying the groundwork for his future research. His educational trajectory showcases a commitment to innovation and excellence, equipping him with the expertise needed to bridge academia and industry. Through continuous learning and research advancements, Dr. Lung has remained at the forefront of emerging technologies in computing and engineering.

Professional Experience

Dr. Lung has a rich professional background in both academia and industry. He is currently a Professor at Carleton University, where he has been a faculty member since 2001. Before becoming a full professor in 2015, he served as an Associate Professor in the same department. His industry experience includes senior roles at Nortel Networks, where he worked as a Senior Software Designer and Network Engineer on Optical Packet Interworking and MPLS-based Traffic Engineering. He was also a Senior Software Architecture Engineer at Nortel’s Software Engineering Analysis Lab (SEAL), contributing to critical advancements in software engineering and network technologies. Additionally, he has worked as an Instructor and Research Assistant at Arizona State University and a Software Engineer at Electronics Research & Service Organization in Taiwan. His diverse career path reflects his versatility and expertise in both theoretical and applied computing disciplines.

Research Interests

Dr. Lung’s research focuses on machine learning, cybersecurity, software engineering, and network optimization. His work in machine learning-based intrusion detection systems (IDS) has led to the development of AI-driven security solutions for SCADA and power systems. Additionally, his research on knowledge graphs and unstructured data processing has contributed to advancements in data-driven decision-making. His expertise extends to network traffic analysis, software reliability engineering, and intelligent data sampling, with applications in forest fire detection, industrial automation, and smart city infrastructures. His interdisciplinary approach has fostered collaborations with academic institutions, industry partners, and government agencies, ensuring that his research has real-world impact. By integrating AI, cybersecurity, and software engineering principles, Dr. Lung continues to explore innovative solutions to modern technological challenges.

Awards and Honors

Dr. Lung has received numerous accolades throughout his career, with his most notable recognition being listed among the world’s top 2% most-cited scholars (Stanford-Elsevier, 2022 & 2023). This honor reflects the global impact of his research and his contributions to computer science and engineering. Additionally, he is a registered Professional Engineer (P.Eng.) in Ontario, demonstrating his adherence to the highest professional standards in engineering. Over the years, he has received multiple best paper awards, research grants, and industry recognitions for his work in machine learning, cybersecurity, and network optimization. His mentorship of students and early-career researchers has also been acknowledged through teaching excellence awards and faculty recognitions. With a distinguished academic and professional career, Dr. Lung continues to push the boundaries of innovation in computing and engineering, solidifying his position as a leading researcher in the field.

Conclusion

Dr. Chung-Horng Lung is a highly qualified and impactful researcher, making significant contributions in Computer Science, Machine Learning, and Network Engineering. His strong publication record, industry experience, and citation impact make him a strong contender for the Best Researcher Award. Addressing minor gaps in funding details, patents, and international collaborations could further strengthen his case.

Publications Top Noted

📖 Journal Articles

1️⃣ In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey 🏆

• Author(s): Zhang, Z., Lung, C.-H., Wei, X., Chatterjee, S., Zhang, Z.
• Year: 2023
• Citations: 41 🔥
• Published in: IEEE Internet of Things Journal

2️⃣ iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks 🧠

• Author(s): Zhang, Z., Wei, X., Lung, C.-H., Zhao, Y.
• Year: 2023
• Citations: 17 📈
• Published in: IEEE Internet of Things Journal

3️⃣ Knowledge Graph Generation and Application for Unstructured Data Using Data Processing Pipeline 🤖

• Author(s): Sukumar, S.T., Lung, C.-H., Zaman, M., Panday, R.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: IEEE Access

🎤 Conference Papers

4️⃣ A Federated Learning Framework Based on Spatio-Temporal Agnostic Subsampling (STAS) for Forest Fire Prediction 🔥

• Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Sampalli, S., Ravichandran, T.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: IEEE COMPSAC 2024

5️⃣ Comparative Analysis of Real-Time Data Processing Architectures: Kafka versus MQTT Broker in IoT 📡

• Author(s): Ho, C.L.D., Lung, C.-H., Mao, Z.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: IEEE ICEIB 2024

6️⃣ DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering – an Experiment Report 🛡️

• Author(s): Marleau, S., Rahman, P., Lung, C.-H.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: IEEE ICEIB 2024

7️⃣ Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling 🔥📊

• Author(s): Tavakoli, F., Naik, K., Zaman, M., Lung, C.-H., Ravichandran, T.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: International Conference on Agents and Artificial Intelligence

8️⃣ Forest Fire Prediction Using Multi-Source Deep Learning 🌲🔥

• Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Purcell, R., Sampalli, S.
• Year: 2024
• Citations: 0 (New Publication) 🚀
• Published in: LNICST Conference Proceedings

9️⃣ A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction 🌍🔥

• Author(s): Kaur, P., Naik, K., Purcell, R., Zaman, M., Mutakabbir, A.
• Year: 2023
• Citations: 1 📊
• Published in: IEEE Big Data 2023

🔟 Unstructured Transportation Safety Board Findings Categorization Using the Knowledge Graph Pipeline 🚗📊

• Author(s): Panday, R., Lung, C.-H.
• Year: 2023
• Citations: 1 🏆
• Published in: IEEE Big Data 2023