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

Assist. Prof. Dr. Anusuyah Subbarao | Digital Transformation | Research Excellence Award

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Assist. Prof. Dr. Anusuyah Subbarao | Digital Transformation | Research Excellence Award

Multimedia University | Malaysia

Ts. Dr. Anusuyah Subbarao is an accomplished Assistant Professor with more than two decades of academic experience in Software Engineering, Computer Science, and Information Technology. She holds a PhD from Universiti Teknologi Malaysia, a Master of Science in Computer Science majoring in Software Engineering, and a Bachelor’s degree in Computer Science from Universiti Putra Malaysia. Her research interests span digital transformation, healthcare informatics, enterprise resource planning, cloud computing risks, global software development, qualitative methodologies, and digital literacy. Throughout her career, she has contributed extensively to research through journal articles, book chapters, and conference proceedings, earning numerous national and international awards for teaching, innovation, and research excellence. She has successfully secured internal and external research grants and served as a reviewer for high-impact journals. Her innovation projects have received multiple gold, silver, and bronze medals at prestigious exhibitions such as InventX and RICES. With strong expertise in digital health, social innovation, ERP adoption, and technology acceptance, she actively supports technological advancement and community empowerment through impactful research. Recognized for her dynamic communication skills and commitment to transformative education, she continues to inspire learners and professionals while advancing cutting-edge research in digital technologies.

Profiles:  ORCID

Featured Publications

Rahman, H. M. M., Islam, J., Malarvizhi, C. A. N., Khan, N., Subhan, K. M. A., & Subbarao, A. (2026). Women empowerment in war and nation building on the basis of Tahmima Anam’s trilogy. Salud, Ciencia y Tecnología.

Murugasu, U. S. G., & Subbarao, A. (2025). Empirical method for developing a hyper-personalization artifact. Engineering Applications of Artificial Intelligence.

Subbarao, A., Siddika, A., Fathullah, M. A., & Sanwani, M. A. B. (2025). The role of mobile applications in shaping digital transformation in higher education among Generation I: A bibliographic study. Information.

Tang, Y., Subbarao, A., & Aw, A. T. H. (2025). A critical review of Theory of Planned Behavior in knowledge payment. International Journal of Management, Finance and Accounting.

Subbarao, A., Khan, N., Siddika, A., Fathullah, M. A., Sanwani, M. A., Adam, F. F., Ferezagia, D. V., & Altamira, M. B. (2025). Digital transformation in higher education: Enhancing support services through mobile apps. HighTech and Innovation Journal.

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Barriers to and facilitators of technology adoption in emergency departments: A comprehensive review. International Journal of Environmental Research and Public Health.

Abd Sukor, N. I., & Subbarao, A. (2025). Unveiling the pathways: Exploring influential factors shaping intentions to engage with ChatGPT. International Journal of Management, Finance and Accounting.

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Barriers and facilitators of technology adoption in emergency departments: A comprehensive review [Preprint]. Preprints.

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding psychosocial barriers to healthcare technology adoption: A review of TAM technology acceptance model and unified theory of acceptance and use of technology (UTAUT) frameworks. Healthcare.

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding psychosocial barriers to healthcare technology adoption: A review of TAM and UTAUT frameworks [Preprint]. Preprints.

Khairuddin, I. K., & Subbarao, A. (2024). Examining the offline shopping preferences of millennial women: A comprehensive review. International Journal of Management, Finance and Accounting.

Mr. Naveen Kumar | Performance Management | Best Scholar Award

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Mr. Naveen Kumar | Performance Management | Best Scholar Award

Jawaharlal Nehru university, New Delhi | India 

Profile: Google Scholar 

Featured Publications

Kumar, N., & Karambir, R. (2012). A comparative analysis of PMX, CX and OX crossover operators for solving traveling salesman problem. International Journal of Latest Research in Science and Technology, 1(2), 98–101.

Kumar, N., & Chaudhary, A. (2024). Surveying cybersecurity vulnerabilities and countermeasures for enhancing UAV security. Computer Networks, 252, 110695.

Kumar, N. (2012). A genetic algorithm approach to study traveling salesman problem. Journal of Global Research in Computer Science, 3(3), 33–37.

Kumar, N., Chaudhary, V., & Dubey, S. K. (2025). Cybersecurity and emerging technologies: Challenges and opportunities. In Cybersecurity preparedness among Indian firms: Opportunities, challenges, and strategies (pp. xx–xx).

Kumar, N., Kumar, G., & Chaudhary, V. (2024). Redefining national security threats in cyberspace: A challenging problem. In Proceedings of the International Seminar on Emerging Threats to National Security: Cyber and Information Warfare (pp. xx–xx).

Dr. Hai Xue | Edge computing | Best Researcher Award

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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