Assoc. Prof. Dr. Niaz Abdolrahim | Best Researcher Award

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Assoc. Prof. Dr. Niaz Abdolrahim | Best Researcher Award

University of Rochester | United States

Dr. Niaz Abdolrahim is an accomplished materials scientist and Assistant Professor in the Department of Mechanical Engineering at the University of Rochester, where she leads pioneering research in multiscale modeling, nanomechanics, and computational materials science. She earned her Ph.D. in Mechanical Engineering and has since developed a strong research portfolio that integrates atomistic simulations, machine learning, and continuum mechanics to study deformation mechanisms, structural phase transformations, and the design of high-performance nanostructured materials. With over 44 published documents, more than 717 citations, and an h-index of 16, her scholarly contributions have been widely recognized in the fields of materials modeling and nanostructure design. Dr. Abdolrahim has secured multiple NSF-funded projects, including the study of stress-assisted phase transformations and data-driven analysis of lattice dynamics. Her work has been published in prestigious journals such as Acta Materialia, npj Computational Materials, Physical Review B, and ACS Applied Nano Materials. Her research interests encompass nanostructured metals, deformation physics, data-driven materials design, and high-performance alloys. Dr. Abdolrahim’s innovative contributions continue to advance the understanding of mechanical behavior in nanoscale systems and establish her as a leading figure in computational materials science and multiscale simulation.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Mostafa, A., Qian, S., Li, F., Rabkin, E., & Abdolrahim, N. (2026). Bending-induced phase transformations and penta-twinning in molybdenum: From nano to microscale. Acta Materialia, 264, 121646. https://doi.org/10.1016/j.actamat.2025.121646

Karami, S., Kum, T. B., Kirmani, A. R., & Abdolrahim, N. (2025). Proton radiation effects in indium oxide using cascade molecular dynamics simulations. APL Energy, 4(9), 0266752. https://doi.org/10.1063/5.0266752

Alvarez, A., Abdolrahim, N., & Singh, S. (2025). Anomalous elastic softening in ferroelectric hafnia under pressure. Physical Review B, 111(6), 064106. https://doi.org/10.1103/PhysRevB.111.064106

Mostafa, A., Vu, L., Guo, Z., Shargh, A. K., Dey, A., Askari, H., & Abdolrahim, N. (2024). Phase-transformation assisted twinning in molybdenum nanowires. Computational Materials Science, 237, 113273. https://doi.org/10.1016/j.commatsci.2024.113273

Salgado, J. E., Lerman, S., Du, Z., Xu, C., & Abdolrahim, N. (2023). Automated classification of big X-ray diffraction data using deep learning models. npj Computational Materials, 9(1), 214. https://doi.org/10.1038/s41524-023-01164-8

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

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Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Anurag University | India

Dr. Shravan Kumar Rudrabhatla is an Assistant Professor at Anurag University, Hyderabad, specializing in fluid dynamics and artificial neural networks. He earned his Ph.D. in Applied Mathematics from the National Institute of Technology (NIT), Warangal in 2023 under the supervision of Prof. D. Srinivasacharya, focusing on the artificial neural network treatment of Casson fluid flow over a radially stretching sheet. His research integrates deep learning, computational fluid dynamics, and heat and mass transfer modeling, contributing to the understanding of complex non-Newtonian flows. Dr. Rudrabhatla has authored 6 research articles, accumulated 49 citations from 43 documents, and achieved an h-index of 4, as indexed by Scopus. His recent works include publications in European Journal of Mechanics B/Fluids, Physics of Fluids, Mathematical Models and Computer Simulations, and Journal of Thermal Analysis and Calorimetry. He has participated in numerous faculty development programs, workshops, and GIAN courses focused on machine learning and computational modeling. His academic journey is complemented by strong technical skills in Python, MATLAB, and C++, and a teaching background spanning over a decade. Dr. Rudrabhatla’s work continues to advance the intersection of mathematics, fluid mechanics, and artificial intelligence, contributing significantly to modern computational sciences.

Profiles : Orcid | Google Scholar | Scopus

Featured Publications

Srinivasacharya, D., & Kumar, R. S. (2022). Artificial neural network modeling of the Casson fluid flow over unsteady radially stretching sheet with Soret and Dufour effects. Journal of Thermal Analysis and Calorimetry, 147, 14891–14903. https://doi.org/10.1007/s10973-022-11694-w

Srinivasacharya, D., & Shravan Kumar, R. (2023). Neural network analysis for bioconvection flow of Casson fluid over a vertically extending sheet. International Journal of Applied and Computational Mathematics, 9(5), 80. https://doi.org/10.1007/s40819-023-01556-w

Srinivasacharya, D., & Kumar, R. S. (2023). An artificial neural network solution for the Casson fluid flow past a radially stretching sheet with magnetic and radiation effect. Mathematical Models and Computer Simulations, 15(5), 944–955. https://doi.org/10.1134/S2070048223050101

Nallapu, S., Sneha, G. S., & Kumar, S. R. (2018). Effect of slip on Jeffrey fluid flow through an inclination tube. Journal of Physics: Conference Series, 1000(1), 012041. https://doi.org/10.1088/1742-6596/1000/1/012041

Rudrabhatla, S. K., & Srinivasacharya, D. (2025). Deep learning framework for Casson fluid flow: A PINN approach to heat and mass transfer with chemical reaction and viscous dissipation. European Journal of Mechanics – B/Fluids, 204401. https://doi.org/10.1016/j.euromechflu.2025.204401

Assoc. Prof. Dr. Xiaoping Yi | Best Researcher Award

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Assoc. Prof. Dr. Xiaoping Yi | Best Researcher Award

University of Science and Technology Beijing | China

Dr. Xiaoping Yi is a materials scientist specializing in first-principles calculations and molecular dynamics of lithium batteries and solid electrolytes, with strong experience in both simulation and experimental design. She earned her PhD in Chemistry from the University of Science and Technology Beijing (2018–2023) and also conducted research at the University of Birmingham, UK, focusing on novel inorganic solid electrolytes, polymer electrolyte design, and silicon-based anodes. After completing her doctorate, she joined the Institute of Physics at the Chinese Academy of Sciences as a postdoctoral researcher (2023–2025), and in 2025 she became Associate Professor at the University of Science and Technology Beijing. Her research interests include nanomaterials design, solid-state lithium/sodium ion batteries, interface electrochemistry, catalytic mechanisms, synchrotron spectroscopy, electron microscopy, and computational materials science. She has published over 25 peer-reviewed SCI articles in high-impact journals (e.g. Advanced Energy Materials, Energy Storage Materials), and her work is recognized for integrating theory and experiment to address performance and safety trade-offs in all-solid-state batteries. Her representative recent work is “Achieving Balanced Performance and Safety for Manufacturing All‐Solid‐State Lithium Metal Batteries by Polymer Base Adjustment” (Adv. Energy Mater., 2025). Her current h-index is approximately 13 with ~1,164 citations (estimated) according to public metrics. She has received recognition for her contributions in battery materials and solid-state electrolytes. Looking ahead, she aims to drive breakthroughs in safe, high-energy density solid-state battery systems via multiscale modeling and experimental validation.

Profile : Orcid

Featured Publications

Yi, X., Li, H.*, Yang, Y., Xiao, K., Zhang, S., Wang, B., Wu, N., Cao, B., Zhou, K., Zhao, X., Leong, K. W., et al. (2025). Achieving balanced performance and safety for manufacturing all-solid-state lithium metal batteries by polymer base adjustment. Advanced Energy Materials, 15(3), 2404973.

Yi, X., Li, H.*, et al. (2025). Strategically tailored polyethylene separator parameters enable cost-effective, facile, and scalable development of ultra-stable liquid and all-solid-state lithium batteries. Energy Storage Materials, 77, 104191.

Chen, N., Yi, X., Liang, Y., et al. (2025). Terminal steric shielding resolves solvent co-intercalation degradation: Molecularly tailored weak-solvation electrolytes for structurally durable K-ion batteries. Chemical Engineering Journal. (Accepted).

Qi, G., Yi, X.*, et al. (2025). Electrochemical-mechanical coupled phase-field modeling for lithium dendrite growth in all-solid-state lithium metal batteries. Journal of Energy Chemistry, 110, 80–87.

Chen, N., Yi, X., Liang, Y., et al. (2024). Dual-steric hindrance modulation of interface electrochemistry for potassium-ion batteries. ACS Nano, 18(32), 32205–32214.

Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

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Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

Researcher at University of Ngaoundéré | Cameroon

Dr. Ulrich Ngnassi Nguelcheu is a researcher at the University of Ngaoundéré, Cameroon, specializing in artificial intelligence applications in renewable energy systems. With 11 publications, 1,302 reads, and 5 citations (h-index: 2), he has made valuable contributions to intelligent control and data-driven optimization for sustainable energy technologies. He holds a doctorate in Artificial Intelligence applied to Renewable Energies, focusing on enhancing the performance and reliability of electromechanical systems through AI-based modeling and simulation. His research experience covers wind energy systems, maintenance optimization, reliability analysis, and composite material development. Among his notable works are studies on the use of artificial neural networks for improved wind turbine control and the optimization of preventive maintenance using genetic algorithms, published in leading journals such as Engineering Applications of Artificial Intelligence. Dr. Nguelcheu actively collaborates with researchers across Cameroon and internationally, emphasizing sustainable and intelligent energy management. His research interests include machine learning for energy systems, renewable energy integration, and smart maintenance strategies. He is dedicated to advancing innovative and eco-efficient technologies that support the global shift toward clean and sustainable energy.

Profiles : Research Gate | Scopus

Featured Publications

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Effa, J. Y. (2025). Harnessing artificial neural networks for improved control of wind turbines based on brushless doubly fed induction generator. Engineering Applications of Artificial Intelligence, 154, 110925.

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Dountio, T. (2023). Literature review on the control of brushless doubly-fed induction machines. Global Journal of Engineering and Technology Advances, 16(3), 51–69.

Ngnassi Djami, A. B., Nguelcheu, U. N., & Yamigno, S. D. (2023). Formulation, characterization and future potential of composite materials from natural resources: The case of kenaf and date palm fibers. Online Journal of Mechanical Engineering.