Ramkumar Kalyanaraman | Computer Science | Outstanding Scientist Award

Published on by Research Awards

Prof. Dr. Ramkumar Kalyanaraman | Computer Science | Outstanding Scientist Award

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology | India

Dr. K. Ramkumar is a distinguished academician, researcher, and innovator with over twenty-three years of rich teaching and research experience in the field of Engineering and Computer Science. He is presently serving as a Professor in the Department of Computer Science and Engineering at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India. His illustrious academic journey is marked by consistent dedication to research, innovation, and academic excellence. He obtained his Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli, in 2018, specializing in Security and Privacy in Cloud Computing, a domain of critical importance in the digital era. To further enhance his expertise and broaden his research perspectives, he pursued a Post-Doctoral Fellowship (PDF) at the Federal University of Ceará, Fortaleza, Brazil, in 2023, focusing on Artificial Intelligence and Biomedical Data Analytics. Throughout his career, Dr. Ramkumar has held several prestigious leadership positions in academia, contributing extensively to institutional growth and quality enhancement. He has served as Professor and Head of the Department (CSE) at Rajalakshmi Institute of Technology, Chennai, and earlier as Professor and Associate Dean (Engineering & Technology) at SRM University, Delhi-NCR, Sonepat, Haryana, where he also chaired the Board of Studies. His earlier academic affiliations include SRM Institute of Science and Technology (Vadapalani Campus, Chennai), Kings Engineering College, and Indira Institute of Engineering and Technology, where he also functioned as Director of Placements. His industry exposure includes working with global technology companies such as Satyam Computer Services Ltd. and LogicaCMG Pvt. Ltd. (now CGI), where he held roles as Associate Consultant, IT Consultant, and Project Leader, leading large technical teams across onshore and offshore environments. Dr. Ramkumar’s research interests encompass a broad spectrum of emerging technologies including Cloud Computing Security, Artificial Intelligence, Machine Learning, IoT Frameworks, Blockchain Systems, and Biomedical Data Analytics. His prolific research output includes numerous publications in SCI, Scopus, and Web of Science-indexed journals, with several articles published in reputed platforms such as Elsevier, Springer, Taylor & Francis, and IEEE. His recent works focus on AI-based diabetic risk prediction, intelligent human activity recognition for assistive technologies, quantum image encryption, and deep learning applications for medical imaging. According to his Google Scholar profile, Dr. Ramkumar has achieved over 1,327 citations, with an h-index of 18 and an i10-index of 24, reflecting the global impact and scholarly recognition of his research contributions. His academic influence extends beyond publications, as he has co-supervised several Post-Doctoral Fellows at the Singapore Institute of Technology, demonstrating his commitment to mentoring and nurturing emerging researchers. Dr. Ramkumar has also published and been granted multiple patents across domains such as wireless sensor networks, mobile ad hoc networks, IoT-based monitoring systems, AI-driven diagnostic tools, and environmental pollution control mechanisms, reflecting his strong inclination toward innovation-driven applied research. Dr. K. Ramkumar stands as a dynamic academic leader whose contributions bridge academia, research, and industry, exemplifying excellence in technological innovation, knowledge dissemination, and professional leadership. His remarkable blend of teaching expertise, research achievements, and administrative acumen continues to inspire students, scholars, and peers across the global academic and scientific community.

Profiles: Scopus | Google Scholar

Featured Publications

Ramkumar, K. (2022). A comparative analysis of methods of endmember selection for use in subpixel classification: A convex hull approach. Computational Intelligence and Neuroscience, 2022, Article ID 3770871

Ramkumar, K., Ananthi, N., Brabin, D. R. D., Goswami, P., Baskar, M., & Bhatia, K. K. (2021). Efficient routing mechanism for neighbour selection using fuzzy logic in wireless sensor network. Computers & Electrical Engineering, 94, 107365.

Banerjee, U., Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to dimension eight: Integrating out heavy scalar(s). The European Physical Journal Plus, 139(2), 1–29.

Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to dimension eight: Integrating out heavy fermion(s). Nuclear Physics B, 1000, 116488.

Ramkumar, K., Medeiros, E. P., Dong, A., de Albuquerque, V. H. C., Hassan, M. R., & Hassan, M. M. (2024). A novel deep learning framework based Swin transformer for dermal cancer cell classification. Engineering Applications of Artificial Intelligence, 133, 108097.

Banerjee, U., Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to any mass-dimension for non-degenerate scalars and fermions including light–heavy mixing. The European Physical Journal Plus, 139(2), 169.

 

Shaowei Wang | Computer Science | Distinguished Scientist Award

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Mr. Shaowei Wang | Computer Science | Distinguished Scientist Award

Guangzhou University | China

Dr. Shaowei Wang is currently an Associate Professor at the School of Artificial Intelligence, Guangzhou University. He earned his Ph.D. from the University of Science and Technology of China (USTC) in 2019. Prior to his academic appointment, he worked as an Applied Researcher at Tencent Technology (Shenzhen), where he contributed to industry-grade privacy-preserving solutions. His research primarily focuses on privacy-preserving computing, federated learning, and AI security, with a strong emphasis on differential privacy techniques and secure data sharing protocols. Dr. Wang has an impressive scholarly record, having authored or co-authored over 40 peer-reviewed papers, with 15 publications as the first or corresponding author in top-tier venues such as USENIX Security, IEEE S&P, INFOCOM, and ICDE. As of September 2025, his work has garnered 1,106 citations, achieving an h-index of 16 (Google Scholar indexed), reflecting the impact and relevance of his contributions to the field. He has been the Principal Investigator (PI) for six research projects, including three funded by the National Natural Science Foundation of China, and a key researcher in five national-level and regional-level projects. Notable ongoing research includes work on shuffled differential privacy, privacy attacks on pre-trained models, and secure digital identity protocols. His academic excellence and innovation have earned him multiple accolades, including an Honorable Mention Award at USENIX Security, a Top 3% Paper Award at ICASSP, and the First Prize in Natural Sciences from the Guangdong Artificial Intelligence Association. Dr. Wang remains committed to advancing the frontiers of privacy-preserving AI through impactful research, interdisciplinary collaboration, and high-quality publications in the global research community.

Profile: Scopus | Google Scholar

Featured Publications

Wang, S., Huang, L., Nie, Y., Zhang, X., Wang, P., Xu, H., & Yang, W. (2019). Local differential private data aggregation for discrete distribution estimation. IEEE Transactions on Parallel and Distributed Systems, 30(9), 2046–2059.

Xin, B., Yang, W., Geng, Y., Chen, S., Wang, S., & Huang, L. (2020). Private FL-GAN: Differential privacy synthetic data generation based on federated learning. In ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. [pages not provided]). IEEE.

Shen, Y., Huang, L., Li, L., Lu, X., Wang, S., & Yang, W. (2015). Towards preserving worker location privacy in spatial crowdsourcing. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE.

Nie, Y., Yang, W., Huang, L., Xie, X., Zhao, Z., & Wang, S. (2018). A utility-optimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering, 31(4), 655–669.

Wang, S., Huang, L., Nie, Y., Wang, P., Xu, H., & Yang, W. (2018). PrivSet: Set-valued data analyses with local differential privacy. In IEEE INFOCOM 2018 – IEEE Conference on Computer Communications (pp. 1088–1096). IEEE.

Yang, G., Wang, S., & Wang, H. (2021). Federated learning with personalized local differential privacy. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. [pages not provided]). IEEE.

Xin, B., Geng, Y., Hu, T., Chen, S., Yang, W., Wang, S., & Huang, L. (2022). Federated synthetic data generation with differential privacy. Neurocomputing, 468, 1–10.