Dr. DEBADATTA NAIK, Researcher cum Teacher, VIZJA University, Poland.
Dr. Debadatta Naik is a passionate educator, researcher, and expert in Computer Science & Engineering, specializing in social network analysis, community detection, and distributed computing. Currently pursuing a Ph.D. at the prestigious Indian Institute of Technology (ISM), Dhanbad, he has built an academic and research profile that reflects deep proficiency in designing computational strategies for social network analysis. In over a decade of teaching and research experience across institutions in Odisha, he has inspired countless students. As an avid programmer and researcher, he is proficient in C, C++, and Python, making significant contributions to computational theory and network dynamics.
Publication Profile
Scopus
ORCID
🎓 Education Background
Dr. Debadatta Naik holds a Ph.D. in Computer Science & Engineering from the Indian Institute of Technology (ISM), Dhanbad (2017–2024), specializing in computational strategies for social network analysis. Prior to this, he earned an M.Tech. from the National Institute of Technology, Rourkela (2015–2017), focusing on centrality approaches for community detection in social networks. He graduated with a B.Tech. in Information Technology from V.S.S.U.T. (formerly UCE Burla), Sambalpur, in 2007. This strong academic background has laid a solid foundation for his research and teaching career, making him a leading contributor in this field.
👔 Professional Experience
With over a decade of teaching and research experience, Dr. Debadatta Naik has served as a lecturer at Raajdhani Engineering College, Bhubaneswar (2010–2015), and GITA, Bhubaneswar (2007–2008). Currently pursuing doctoral research, he is an integral collaborator with VIZJA University, Warsaw, Poland, exploring advances in social network analysis. His expertise spans programming languages (C, C++), database technologies (MySQL), and theoretical subjects like the theory of computation, data structures, and compiler design. A trusted educator and reviewer, he has conducted reviews for reputable journals such as Social Network Analysis and Mining, Scientific Review, and the Computing Journal.
🏅 Awards and Honors
Throughout his academic and professional journey, Dr. Debadatta Naik has been recognized for excellence. He received a Ph.D. Fellowship (2017–2022) and an M.Tech. Fellowship (2015–2017) from the Government of India through GATE scores of 303 and 477, respectively. During his undergraduate years, he secured first positions in athletic meets and distinguished himself in painting, sketching, and calligraphy competitions at V.S.S.U.T. He was a Golden Jubilee Torch Bearer and served as the ART and Photography Secretary, making significant contributions to campus life. These accomplishments reflect his multidisciplinary talents and commitment to excellence.
🔍 Research Focus
Dr. Debadatta Naik’s research focuses on social network analysis, community detection, link prediction, and workflow scheduling. He develops computational strategies for extracting insights from massive social networks, leveraging tools like Hadoop and MapReduce. His work has been published in top journals such as Simulation Modelling Practice and Theory, Journal of Ambient Intelligence and Humanized Computing, Cluster Computing, and Expert Systems with Applications. By exploring innovative approaches like Quantum-PSO and hybrid optimization methods, he aims to optimize the performance and scalability of complex network analytics for cloud environments, making a significant impact in both academia and industry.
✅ Conclusion
With a solid academic background, deep teaching experience, and a strong research record, Dr. Debadatta Naik has established himself as a dedicated educator and prolific researcher. His work advances the understanding of social network dynamics, while his active role in peer review and collaborative research showcases his ongoing contribution to the scientific community. As he continues to expand the frontiers of computational social network analysis and workflow scheduling, he inspires the next generation of engineers and researchers to pursue excellence and innovation in computer science.
📑 Publication Top Notes
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Hgwomultiqos: A hybrid grey wolf optimization approach for qos‑constrained workflow scheduling in iaas clouds.
Simulation Modelling Practice and Theory, 2025
Cited by: Forthcoming
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Enhanced link prediction using sentiment attribute and community detection.
Journal of Ambient Intelligence and Humanized Computing, 2023
Cited by: 7
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Quantum‑pso based unsupervised clustering of users in social networks using attributes.
Cluster Computing, 2023
Cited by: 3
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Parallel and distributed paradigms for community detection in social networks: A methodological review.
Expert Systems with Applications, 2022
Cited by: 24
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Map‑reduce‑based centrality detection in social networks: An algorithmic approach.
Arabian Journal for Science and Engineering, 2020
Cited by: 21
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Genetic algorithm‑based community detection in large‑scale social networks.
Neural Computing and Applications, 2020
Cited by: 48
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Mr‑ibc: Mapreduce‑based incremental betweenness centrality in large‑scale complex networks.
Social Network Analysis and Mining, 2020
Cited by: 19