Beomju Kim | Power System Stability | Research Excellence Award

Mr. Beomju Kim | Power System Stability | Research Excellence Award

Power System | Korea University | South Korea

Kim Beomju is a Ph.D. integrated program researcher in the Department of Electrical and Electronic Engineering at Korea University, specializing in power and energy systems. He holds a bachelor’s degree in electrical engineering and has developed strong expertise through advanced doctoral training focused on modern power grids. His professional experience includes active roles in nationally and industry funded projects in collaboration with major energy stakeholders, contributing to HVDC operation, offshore wind integration, grid robustness assessment, and system monitoring platforms. His research focuses on power system stability and dynamics, renewable energy integration, inertia estimation, and frequency stability, with publications in internationally indexed journals as well as patented technologies for advanced grid analysis and monitoring. His achievements include research excellence recognitions, patented innovations, and active membership in professional engineering societies. His scholarly impact is reflected in 10,746 citations, an h-index of 41, and an i10-index of 109.

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Featured Publications

Attack Vulnerability of Complex Networks
P. Holme, B. J. Kim, C. N. Yoon, S. K. Han
Physical Review E · Citations: 2564

Growing Scale-Free Networks with Tunable Clustering
P. Holme, B. J. Kim
Physical Review E · Citations: 1511

Synchronization on Small-World Networks
H. Hong, M. Y. Choi, B. J. Kim
arXiv Preprint · Citations: 573

Vertex Overload Breakdown in Evolving Networks
P. Holme, B. J. Kim
Physical Review E · Citations: 338

Factors That Predict Better Synchronizability on Complex Networks
H. Hong, B. J. Kim, M. Y. Choi, H. Park
Physical Review E · Citations: 314

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Assistant Professor | SR University | India

Dr. Swarup Ghosh is an Assistant Professor and Assistant Dean (Research) at the School of Computer Science and Artificial Intelligence, SR University, specializing in computational materials science, condensed matter physics, and AI-driven materials discovery. He earned his Ph.D. in Science from Jadavpur University with a focus on first-principles calculations, following an M.Sc. in Physical Sciences and a B.Sc. in Physics. Dr. Ghosh previously served as a Postdoctoral Research Associate at Jadavpur University and as a faculty member at Sammilani Mahavidyalaya, contributing to advanced computational materials research and student mentorship. His work spans density functional theory, molecular dynamics, many-body perturbation theory, electronic structure simulations, and machine-learning-enabled materials design, resulting in publications in high-impact journals and presentations at prestigious scientific forums. His research includes breakthroughs in 2D and nanomaterials, thermoelectrics, photovoltaics, spintronics, and catalytic systems, emphasizing data-centric scientific innovation. He has been honored with national research fellowships, merit-based academic distinctions, and awards for research excellence, while also serving as a reviewer for reputed international journals and participating in professional training programs and conferences. He maintains a strong scholarly impact, demonstrated by 245 citations, an h-index of 9, and an i10-index of 9, underscoring his growing influence in computational materials science and interdisciplinary research.

Profile: Google Scholar

Featured Publications

Swarup Ghosh*, Predicting photovoltaic efficiency of two-dimensional Janus materials for solar energy harvesting: A combined first-principles and machine learning study. Solar Energy Materials and Solar Cells, Accepted.

Swarup Ghosh*, First-principles study on structural, electronic, optical and photovoltaic properties of Sc₂C-based Janus MXenes for solar cell applications. Materials Today Communications, Accepted.

Swarup Ghosh, Predicting band gaps of ABN₃ perovskites: An account from machine learning and first-principles DFT studies. RSC Advances, Accepted.