University of Shanghai for Science and Technology,
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🎓 Early Academic Pursuits
Dr. Hai Xue embarked on his academic journey in the field of computer engineering with a Bachelor of Science in Information and Communication Engineering from Konkuk University, Seoul, South Korea, in 2014. Driven by an insatiable curiosity for software and computing, he pursued his Master’s degree at Hanyang University, Seoul, where he specialized in Computer and Software under the guidance of Prof. Inwhee Joe. This period was crucial in shaping his foundational knowledge and research skills, which later fueled his contributions to edge computing and network science. Dr. Xue culminated his formal education with a Ph.D. in Computer Engineering from Sungkyunkwan University, Suwon, in 2020, where he worked under the mentorship of Prof. Hee Yong Youn. His doctoral research laid the groundwork for his future breakthroughs in dynamic resource allocation and federated learning.
🌟 Professional Endeavors
Dr. Xue’s professional career is marked by a series of prestigious positions that reflect his growing influence in the field of computer engineering. After earning his Ph.D., he served as a Research Professor at Korea University, Seoul, from September 2020 to September 2021. During this tenure, he collaborated with renowned researcher Prof. Sangheon Pack, contributing significantly to the domains of edge computing and network optimization. In September 2021, he transitioned to his current role as an Assistant Professor at the University of Shanghai for Science and Technology (USST), Shanghai, China. Here, he continues to engage in high-impact research, mentoring young scholars, and advancing cutting-edge technological solutions.
🔮 Contributions and Research Focus
Dr. Xue’s research interests are deeply rooted in dynamic resource allocation, federated learning, and edge computing. His contributions have led to substantial advancements in these areas, including:
- Dynamic Pricing in Edge Offloading: His recent work on dynamic pricing-based near-optimal resource allocation is set to redefine how computational resources are distributed efficiently across networks.
- Energy Harvesting in Edge Computing: His paper on dynamic differential pricing-based edge offloading systems with energy harvesting devices has been accepted by IEEE Transactions on Network Science and Engineering, highlighting his expertise in sustainable and energy-efficient computing.
- Federated Learning Incentive Mechanisms: His study on Yardstick-Stackelberg pricing-based incentive mechanisms for federated learning in edge computing, accepted by Computer Networks, sheds light on optimizing collaborative learning models.
- Neural Network Optimization: His work on dynamic pseudo-mean mixed-precision quantization (DPQ) for pruned neural networks, published in Machine Learning, underscores his ability to push the boundaries of artificial intelligence efficiency.
🏆 Accolades and Recognition
Dr. Xue’s contributions have not gone unnoticed. His publications in high-impact journals such as IEEE Transactions, Computer Networks, and Machine Learning underscore his academic excellence. His research has been classified under prestigious rankings, including CAS Q2 and JCR Q1, affirming its significance within the scientific community. These accolades reflect his unwavering commitment to innovation and the quality of his scholarly output.
🌐 Impact and Influence
Dr. Xue’s research has far-reaching implications in both academia and industry. His work in dynamic pricing mechanisms is influencing how network providers optimize their resource allocation, while his advancements in federated learning are paving the way for more secure and efficient decentralized AI applications. His insights into energy harvesting in edge computing hold promise for sustainable technological solutions, a pressing need in today’s energy-conscious world.
🌟 Legacy and Future Contributions
Looking ahead, Dr. Xue is poised to make even more significant contributions to computer engineering. His ongoing projects aim to refine the synergy between AI and edge computing, ensuring smarter, more adaptive network solutions. As an educator, he remains dedicated to nurturing the next generation of computing professionals, equipping them with the knowledge and skills necessary to tackle future challenges in technology.
📝Notable Publications
Dynamic load balancing of software-defined networking based on genetic-ant colony optimization
Author(s): H. Xue, K.T. Kim, H.Y. Youn
Journal: Sensors
Year: 2019
Detection of falls with smartphone using machine learning technique
Author(s): X. Chen, H. Xue, M. Kim, C. Wang, H.Y. Youn
Journal: 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI)
Year: 2019
Packet Scheduling for Multiple‐Switch Software‐Defined Networking in Edge Computing Environment
Author(s): H. Xue, K.T. Kim, H.Y. Youn
Journal: Wireless Communications and Mobile Computing
Year: 2018
Dynamic pricing based near-optimal resource allocation for elastic edge offloading
Author(s): Y. Xia, H. Xue, D. Zhang, S. Mumtaz, X. Xu, J.J.P.C. Rodrigues
Journal: arXiv preprint arXiv:2409.18977
Year: 2024
DPQ: dynamic pseudo-mean mixed-precision quantization for pruned neural network
Author(s): S. Pei, J. Wang, B. Zhang, W. Qin, H. Xue, X. Ye, M. Chen
Journal: Machine Learning
Year: 2024