Ms. Shaguna Gupta | Cloud Computing Computer Science Research | Best Researcher Award

Published on by Leadership and Management

Ms. Shaguna Gupta | Cloud Computing Computer Science Research | Best Researcher Award

National College of Ireland | Ireland

Shaguna Gupta is a dedicated computer science researcher specializing in cloud computing, distributed systems, and intelligent transportation systems. She is currently pursuing her Ph.D. at Trinity College Dublin (TCD), Ireland, focusing on highway traffic flow optimization using deep reinforcement learning to improve traffic throughput and travel time reliability. She holds a Master of Technology in Cloud Computing with Queuing Theory from Shri Mata Vaishno Devi University (SMVDU), India, and a Bachelor of Engineering from Jammu University, India. Her research experience spans multiple international institutions, including a postgraduate studentship under Science Foundation Ireland’s CRT ADVANCE project, a research internship at the University of Virginia, USA, and project work at the Indian Institute of Science, Bangalore. She has contributed to multiple publications in areas such as rideshare optimization, queuing theory for cloud services, and microservice resilience. As an educator, she has served as an associate faculty member, demonstrator, and lecturer, supervising cloud computing projects and teaching modules on AI, programming, and cybersecurity. Her research interests include cloud computing, traffic management, multi-agent reinforcement learning, and distributed systems. She has received several academic awards, including gold medals, scholarships, and best paper presentations. Committed to advancing technology for societal benefit, she actively participates in workshops, conferences, and community outreach, reflecting a balance of innovation, leadership, and practical impact.

Profile:  Google Scholar 

Featured Publications

Gupta, S., Narang, R., Krishnaswami, K., & Yadav, S. (1994). Plasma selenium level in cancer patients. Indian Journal of Cancer, 31(3), 192–197.

Fusco, V. F., Sancheti, S., & Gupta, S. (1994). Active antenna element design issues. IEE Colloquium on Smart Antennas (Digest No: 1994/182), 9/1–9/7.

Gupta, S., & Fusco, V. F. (1996). Low cross-polarized integrated mixer/phase shifter patch antenna for beamforming applications. 26th European Microwave Conference, 1, 397–400.

Gupta, S., & Pourush, R. K. S. (1999). Technical Notes-4 x 4 planar phased array of circular patch microstrip antenna in plasma environment for on-board applications. Space Technology-Abingdon, 19(2), 97–108.

Gupta, S., Fusco, V. F., & Sancheti, S. (1999). Self-phased re-transmitting integrated mixer antenna array. International Journal of Electronics, 86(2), 207–215.

Gupta, S., Narang, R., & Patel, M. K., Gupta, K., Kumar, K. (2025). Enhanced ECC-driven text encryption scheme using chaotic maps and Rhotrices. Palestine Journal of Mathematics, 14(3).

Gupta, S., & Arora, S. (2018). Queueing system in cloud services management: a survey. International Journal of Pure and Applied Mathematics, 119(12), 12741–12753.

Shaikh, R. (2025). Prediction of resource utilization in cloud computing using machine learning. Dublin, National College of Ireland.

Dr. Uma Jothi | Team Building and Team Management | Excellence in Research

Published on by Leadership and Management

Dr. Uma Jothi | Team Building and Team Management | Excellence in Research

Amrita University, India

Dr. J. Uma is an accomplished academic and researcher in the fields of information technology, cloud computing, artificial intelligence, and cybersecurity. She holds a B.Tech in Information Technology, an M.E. in Computer Science and Engineering with distinction, and has submitted her Ph.D. thesis in Information and Communication Engineering at Anna University. With more than twelve years of academic experience, she has served as Assistant Professor in leading engineering institutions, contributing significantly to teaching, curriculum development, and research mentorship. Her research focuses on cloud resource allocation, deep reinforcement learning, intelligent optimization algorithms, blockchain technologies, IoT-based systems, and data security. She has published impactful journal articles in reputed outlets like Transactions on Emerging Telecommunications Technologies and Springer’s Lecture Notes in Networks and Systems, along with several book chapters and conference papers. Her work includes innovations in heuristic optimization, adversarial defenses in deep learning, and smart healthcare IoMT solutions. She is also a published patent holder in IoT-based agriculture monitoring systems and employee training platforms. Dr. Uma has organized major AICTE- and RGNIYD-funded programs, contributing to national-level capacity building in data science, IoT, and smart city technologies. Her career reflects a strong commitment to advancing research, innovation, and academic excellence.

Profiles:  ORCID

Featured Publications

Dr. Hai Xue | Edge computing | Best Researcher Award

Published on by Leadership and Management

Dr. Hai Xue | Edge computing | Best Researcher Award

University of Shanghai for Science and Technology,

Profile

Google Scholar

🎓 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