Dugki Min | Machine Learning and AI Applications | Best Researcher Award

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Prof Dr. Dugki Min | Machine Learning and AI Applications | Best Researcher Award

Konkuk University | South Korea

Prof. Dr. Dugki Min is a distinguished academic and thought leader in the field of computer science and artificial intelligence. Currently serving as a Full Professor at the Department of Computer Science and Engineering, College of Engineering, and Department Head of Artificial Intelligence at the General Graduate School of Konkuk University, Seoul, South Korea, he has dedicated nearly three decades to education, research, and innovation. His multidisciplinary expertise, encompassing software engineering, intelligent systems, distributed computing, and AI-based smart city applications, positions him as a pioneer in shaping Korea’s digital transformation and global technology collaborations.

Professional Profiles

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Education

Prof. Min holds a Ph.D. and M.S. in Computer Science from Michigan State University, USA, where he developed a strong foundation in systems architecture and distributed computing. He completed his undergraduate studies in Industrial Engineering at Korea University. His education spans both technical and applied domains, enabling a balanced approach to both theoretical advancements and practical innovations.

Experience

Since joining Konkuk University in 1995, Prof. Min has held several leadership roles, including Research Director of the Distributed Multimedia Lab and Department Head for multiple programs in both the College of Engineering and the General Graduate School. His international academic footprint includes appointments as Visiting Professor at Pacific State University, USA, and Vanderbilt University. Beyond academia, he has significantly influenced industry practices through advisory roles at Samsung Electronics, national IT committees, and standardization bodies such as OASIS and the National Information Society Agency. His longstanding contributions to Korea’s software and simulation societies have solidified his reputation as a key figure in technological policymaking and industry collaboration.

Research Interest

Prof. Min’s research focuses on secure, intelligent, and scalable computing systems. His work spans artificial intelligence, distributed multimedia systems, digital twin technologies, edge-cloud computing, disaster-resilient smart cities, and cyber-physical systems. He leads major government-funded projects including AI-based public safety frameworks and digital twin applications for smart urban air mobility (UAM). His current projects emphasize the convergence of AI, bio-inspired frameworks, and real-time urban analytics to develop resilient, adaptive systems for modern cities. He has also delved into IPFS, blockchain, and biometric authentication, driving forward novel approaches in cybersecurity and information infrastructure.

Awards and Recognition

Prof. Min’s contributions have been recognized through numerous research grants and leadership roles in prestigious international conferences, including the International Conference on IT Promotion in Asia and the International Semantic Web Conference. His role as Program Chair and Steering Committee Member across multiple academic and government platforms highlights his global impact. Additionally, his collaborations with government ministries such as the Ministry of Science and ICT have resulted in pivotal national R&D programs, affirming his status as a trusted thought leader in AI and future mobility solutions.

Publications

Enhancing data harvesting systems: Performance quantification of Cloud–Edge-sensor networks using queueing theory

Authors: Jose Wanderlei Rocha, Eder Gomes, Vandirleya Barbosa, Arthur Sabino, Luiz Nelson Lima, Gustavo Callou, Francisco Airton Silva, Eunmi Choi, Tuan Anh Nguyen, Dugki Min et al.
Journal: ICT Express
Year: 2025

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Authors: Israel Araújo, Luis Guilherme Silva, Carlos Brito, Dugki Min, Jae-Woo Lee, Tuan Anh Nguyen, Erico Leão, Francisco A. Silva
Journal: ICT Express
Year: 2025

Transactional dynamics in hyperledger fabric: A stochastic modeling and performance evaluation of permissioned blockchains

Authors: Carlos Melo, Glauber Gonçalves, Francisco Airton Silva, Iure Fé, Ericksulino Moura, André Soares, Eunmi Choi, Dugki Min, Jae-Woo Lee, Tuan Anh Nguyen
Journal: ICT Express
Year: 2025

Multi-head Fusion-based Actor-Critic Deep Reinforcement Learning with Memory Contextualisation for End-to-End Autonomous Navigation

Authors: Seunghyeop Nam, Tuan Anh Nguyen, Eunmi Choi, Dugki Min
Journal: TechRxiv (Preprint)
Year: 2025

Aerial computing: Enhancing mobile cloud computing with unmanned aerial vehicles as data bridges—A Markov chain based dependability quantification

Authors: Francisco A. Silva, Iure Fé, Carlos Brito, G. Araujo, L. Feitosa, Tuan Anh Nguyen, K. Jeon, J.-W. Lee, Dugki Min, Eunmi Choi
Journal: ICT Express
Year: 2024

Deep learning methods for high-level control using object tracking

Authors: Kanmani P., Yuvaraj N., Karthic S., Sri Preethaa K.R., Dugki Min
Journal: Book Chapter in Urban Air Mobility: Intelligent, Safe and Sustainable Systems for Future Transportation
Year: 2024

Efficient Strategies for Unmanned Aerial Vehicle Flights: Analyzing Battery Life and Operational Performance in Delivery Services using Stochastic Models

Authors: Francisco A. Silva, Vandirleya Barbosa, Luiz Nelson Lima, Arthur Sabino, Paulo Rego, Luciano F. Bittencourt, J. Lee, Dugki Min, Tuan Anh Nguyen
Journal: IEEE Access
Year: 2024

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Authors: Gabriel Araújo, Vandirleya Barbosa, Luiz Nelson Lima, Arthur Sabino, Carlos Brito, Iure Fé, Paulo Rego, Eunmi Choi, Dugki Min, Tuan Anh Nguyen et al.
Journal: IEEE Access
Year: 2024

Conclusion

Prof. Dr. Dugki Min exemplifies academic excellence, innovative research, and impactful leadership in computer science and artificial intelligence. His ability to merge theory with application has led to the creation of intelligent systems that address real-world challenges—from traffic prediction and public safety to accessibility and secure computing. With a prolific record of publications, patents, and technology transfer initiatives, he continues to push the boundaries of smart system design and AI integration in urban and industrial ecosystems. His forward-thinking vision, combined with strong collaboration across academia, government, and industry, ensures his enduring influence in Korea and beyond.

Jin Liu | Algorithm Development | Best Researcher Award

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Dr. Jin Liu | Algorithm Development | Best Researcher Award

Dr. Jin Liu | Algorithm Development – University of Leeds | United Kingdom

Dr. Jin Liu is a dedicated and forward-thinking researcher in the field of railway traffic management and intelligent transportation systems. Currently serving as a Research Fellow at the Institute for Transport Studies, University of Leeds, Dr. Liu has established himself as a key contributor to the development of next-generation transport technologies. His work bridges theory and practice, especially in areas of train rescheduling, real-time traffic control, and AI-driven optimisation methods for complex railway systems. With a solid foundation in electrical engineering and deep expertise in rail operation control systems, Dr. Liu continues to push boundaries in transport innovation through multi-agent systems, digital twins, and AI applications.

Professional Profile

SCOPUS

Education

Dr. Liu began his academic journey with a Bachelor of Engineering in Electrical Engineering and Automation from the Harbin Institute of Technology, China. He then pursued his Master’s degree in Electronic and Electrical Engineering at the University of Birmingham, UK, where he completed a significant final-year project on onboard wheel slip protection systems for high-speed rail. Building on his growing interest in rail systems, Dr. Liu remained at Birmingham to complete his Ph.D., focusing on a multi-agent-based approach for resolving real-time train rescheduling problems in large-scale railway networks. His doctoral work laid the groundwork for much of his subsequent research on optimising railway traffic management using intelligent algorithms.

Professional Experience

Dr. Liu’s academic career commenced at Newcastle University as a Research Assistant and later as a Research Associate in the School of Engineering. He transitioned to the University of Leeds in March 2022, where he now contributes to multiple EU and UK-funded transportation projects. His role in these initiatives often involves the development of advanced algorithms, decision-support tools, and data models for improving operational efficiency in rail and road transport. Dr. Liu also provides leadership in technical project deliverables, industrial collaboration, and conference participation. He has built a reputation for integrating academic research with real-world applications, especially in projects related to electric vehicles, nano-emission tracking, and rail timetable optimisation.

Research Interests

Dr. Liu’s research revolves around modelling, analysis, and optimisation of rail traffic operations, including both passenger and freight services. He is particularly interested in developing energy-efficient train operations, train speed profile optimisation, and multi-agent-based modelling for traffic management. His algorithmic expertise covers both exact methods and meta-heuristic approaches, and he is deeply engaged in applying artificial intelligence—especially reinforcement learning—to real-world transportation challenges. Furthermore, his research encompasses railway system requirements analysis, focusing on RAM (Reliability, Availability, Maintainability), safety and security standards, and the verification and validation of novel railway traffic management applications.

Awards and Professional Engagements

Dr. Liu’s contributions to transport research have been recognised with several prestigious awards and professional memberships. Notably, he is the recipient of the Michael Beverley Innovation Fellowship, the International Researcher Mobility Fund, and has served as a guest speaker at the Birmingham Decarbonisation Summer School. His academic impact is evidenced by his selection as a Program Committee Member for the AI4RAILS 2023 conference and as a session chair at the IEEE ITSC 2023. He also plays a significant role in collaborative transportation initiatives, serving as the Early Career Researcher representative for the Network for Innovative Sustainable Transportation Co-Simulation. Furthermore, his work has been acknowledged among the top ten most downloaded papers of 2023 in his field.

Dr. Liu is an Associate Fellow of the UK’s Higher Education Academy and maintains an active publishing profile, with his research appearing in prestigious journals such as Applied Sciences, IET Intelligent Transport Systems, and Journal of Rail Transport Planning & Management. His research output includes notable work on reinforcement learning applications in train scheduling, system validation in virtual environments, and the assessment of railway operation impacts under hazardous conditions. Through his ongoing projects with European and UK stakeholders—such as Shift2Rail, Network Rail, and the EPSRC—Dr. Liu continues to contribute meaningfully to shaping the future of sustainable, intelligent, and resilient transportation systems.

Publications

A Multi-agent Based Approach for Railway Traffic Management Problems

Authors: J. Liu, L. Chen, C. Roberts, Z. Li, T. Wen
Journal/Conference: 2018 International Conference on Intelligent Rail Transportation (ICIRT)
Year: 2019

Algorithm and peer-to-peer negotiation strategies for train dispatching problems in railway bottleneck sections

Authors: J. Liu, L. Chen, C. Roberts, G. Nicholson, B. Ai
Journal: IET Intelligent Transport Systems
Year: 2019

Use cases for obstacle detection and track intrusion detection systems in the context of new generation of railway traffic management systems

Authors: P. Hyde, C. Ulianov, J. Liu, M. Banic, M. Simonovic, D. Ristic-Durrant
Journal: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Year: 2021

Novel approach for validation of innovative modules for railway traffic management systems in a virtual environment

Authors: J. Liu, C. Ulianov, P. Hyde, A. L. Ruscelli, G. Cecchetti
Journal: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Year: 2022

Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems

Authors: A. Magnien, G. Cecchetti, A. L. Ruscelli, P. Hyde, J. Liu, S. Wegele
Journal: Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification (Book/Conference Proceedings)
Year: 2022