Assoc. Prof. Dr. Mümine KAYA KELEŞ | Women Researcher Award

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Assoc. Prof. Dr. Mümine KAYA KELEŞ | Women Researcher Award

Adana Alparslan Türkeş Science and Technology University | Turkey

Mümine Kaya Keleş is a researcher at Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, where she specializes in Veri Madenciliği (Data Mining), Uzaktan Eğitim (Distance Education), and Metin/Doküman İşleme (Text/Document Processing), with additional work in plagiarism detection. Her scholarly contributions span data mining applications in health, engineering, and education, including breast cancer prediction, feature selection, machine learning–based performance estimation, and streamflow forecasting. She has also made notable advances in distance education systems, the integration of assessment tools into learning management platforms, and text similarity detection. Her research includes the application of optimization algorithms such as artificial bee colony, binary anarchic society optimization, and binary black widow approaches for feature selection across diverse datasets, as well as studies on data mining impacts on various sectors and open-source tools. She has contributed to projects involving microstrip antenna classification, concrete strength prediction, Alzheimer’s disease diagnosis using volumetric data, and productivity prediction in construction. With verified email affiliation at atu.edu.tr, she continues to develop innovative solutions at the intersection of data mining, machine learning, and educational technologies, maintaining a strong publication record across interdisciplinary applications.

Profile : Google Scholar

Featured Publications

Keleş, M. K. (2019). Breast cancer prediction and detection using data mining classification algorithms: A comparative study. Tehnički vjesnik, 26(1), 149–155.

Umit, K., Esra, S. E., & Mumine, K. K. (2023). Binary Anarchic Society Optimization for Feature Selection. Romanian Journal of Information Science and Technology, 26(3–4), 351–364.

Kaya, M., & Özel, S. A. (2015). Integrating an online compiler and a plagiarism detection tool into the Moodle distance education system for easy assessment of programming assignments. Computer Applications in Engineering Education, 23(3), 363–373.

Keleş, M. K., & Özel, S. A. (2016). A review of distance learning and learning management systems. Virtual Learning.

Kaya, M. (2012). Distance education systems used in universities of Turkey and Northern Cyprus. Procedia – Social and Behavioral Sciences, 31, 676–680.

Assoc. Prof. Dr. M. Abdul | Research Excellence Award

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Assoc. Prof. Dr. M. Abdul | Research Excellence Award

Quanzhou University of Information Engineering | China

Muhammad Abdul is a researcher specializing in boson sampling, machine learning, ultracold atoms, high-resolution imaging systems, quantum technology involving surface acoustic waves, quantum phase transitions, nonlinear dynamical systems, and the invention of new materials. He earned his PhD from the University of Science and Technology of China, Hefei, where he worked on ultracold atoms in optical lattices, nonlinear optics, photonic devices, quantum networks, and boson sampling. He previously completed an M.Phil in Electronics at Quaid-i-Azam University. His professional experience includes serving as a Researcher at the University of Electronic Science and Technology of China; Assistant Professor at Sichuan University; Research Associate at Quaid-i-Azam University; Visiting Faculty at the Federal Urdu University; Lecturer at St. Mary College and the Punjab Group of Colleges; and High School Science Teacher at Down High School Mailsi. His research activities span mathematical modeling of nonlinear systems, materials development, and improvements in medication, supported in part by funding for developing a general dynamical model. He has contributed extensively to peer review across major journals and continues to advance interdisciplinary science across China, the United States, and the United Kingdom through research, teaching, and collaboration.

Profile : Orcid

Featured Publications

Abdul, M., Ko, C., Ismail, M. A., Ben Khalifa, S., Alsaif, N. A. M., Chebaane, S., Akbar, J., & Allakhverdiev, S. I. (2026). Development of rare earth metal-supported manganese selenide (MnSe₂-Nd₂O₃) heterostructure enabling robust hydrogen evolution reaction. Fuel. https://doi.org/10.1016/j.fuel.2025.136948

Abdul, M., Zhang, M., Ma, T., Alotaibi, N. H., Mohammad, S., & Luo, Y.-S. (2025). Facile synthesis of Co₃Te₄–Fe₃C for efficient overall water-splitting in an alkaline medium. Nanoscale Advances. https://doi.org/10.1039/D4NA00930D

Abdul, M., Kuo, C.-T., Ismail, M. A., Ben Khalifa, S., Alsaif, N. A. M., Chebaane, S., Shareef, M., & Shehzadi, A. (2025). Facile synthesis of novel WO₃·H₂O@Al-MOF nanocomposite for enhanced electrocatalytic hydrogen and oxygen evolution. Electrochimica Acta. https://doi.org/10.1016/j.electacta.2025.147714

Sardar, S., Nazeer, S., Naeem, F., Ben Khalifa, S., Chebaane, S., Saidani, T., Ismail, M. A., & Abdul, M. (2025). Se-decorated TiC/TiO₂ nanocomposite for overall water-splitting in alkaline medium. Fuel. https://doi.org/10.1016/j.fuel.2025.135672

Abdul, M., Ko, C., Tang, X., Ben Khalifa, S., Alsaif, N. A. M., Chebaane, S., & Akbar, J. (2025). S-scheme MnO₂–MnS₂@C heterostructure for environmental and biological applications. Ceramics International. https://doi.org/10.1016/j.ceramint.2025.09.284

Mr. Farshad Sadeghpour | Editorial Board Member

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Mr. Farshad Sadeghpour | Editorial Board Member

Petroleum University of Technology | Iran

Farshad Sadeghpour is a geomechanics and reservoir engineering researcher at the Petroleum University of Technology (PUT), known for his contributions to underground gas storage, petrophysics, and CO₂ geological sequestration. He has authored multiple peer-reviewed publications covering geomechanical upscaling, fracture development under stress, anisotropic rock behavior, storage-efficiency modeling, and petrophysical parameter estimation. His current Google Scholar record lists 22 citations, an h-index of 3, and 6 indexed documents, reflecting his growing influence in subsurface engineering. He holds academic training in petroleum engineering from PUT and additional postgraduate research experience from the Islamic Azad University, Science & Research Branch. His research experience includes collaborative studies on elastic property prediction, machine-learning-based evaluation of CO₂ storage feasibility, and advanced triaxial testing for characterizing anisotropic formations. His work demonstrates strong expertise in integrating experimental, computational, and data-driven approaches to solve complex reservoir challenges. His research interests include geomechanics, underground storage, CO₂ sequestration, petrophysical modeling, machine learning, and rock mechanics. Although still early in his career, his contributions indicate promise for impactful advancements in sustainable subsurface energy systems. Overall, Farshad Sadeghpour is an emerging researcher dedicated to improving geological storage, reservoir characterization, and the scientific foundations of low-carbon energy technologies.

Profile : Google Scholar

Featured Publications

Sadeghpour, F., Darkhal, A., Gao, Y., Motra, H. B., Aghli, G., & Ostadhassan, M. (2024). Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media. Geoenergy Science and Engineering, 239, 212915.

Aghli, G., Aminshahidy, B., Motra, H. B., Darkhal, A., Sadeghpour, F., … (2024). Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt. Journal of Rock Mechanics and Geotechnical Engineering, 16(11), 4491–4503.

Sadeghpour, F. (2025). Storage efficiency prediction for feasibility assessment of underground CO₂ storage: Novel machine learning approaches. Energy, 324, 136040.

Iranfar, S., Sadeghpour, F., Manshad, A. K., Naderi, M., & Shakiba, M. (2025). An eigenvalue-driven framework for the ranking and selection of optimal geological CO₂ storage sites. Results in Engineering, 106770.

Sadeghpour, F., Motra, H. B., Sethi, C., Wind, S., Hazra, B., Aghli, G., … (2025). Elastic properties of anisotropic rocks using a stepwise loading framework in a true triaxial testing apparatus. Geoenergy Science and Engineering, 251, 213883.

Mr. Harish Verma | Best Researcher Award

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Mr. Harish Verma | Best Researcher Award

Indian Institute of Technology (Banaras Hindu University) Varanasi | India

Dr. Harish Verma holds a B.Sc (UG), B.Ed, M.Sc (PG), and M.Phil in Physics and has qualified the CSIR-NET JRF examination. He is currently pursuing a Ph.D. in energy storage, dielectric materials, density functional theory (DFT), artificial intelligence (AI), and machine learning (ML) at the Indian Institute of Technology (BHU), Varanasi. His research focuses on the synthesis and characterization of advanced functional materials such as oxide perovskites, spinels, and graphene-based nanocomposites for dielectric and electrochemical energy storage applications. Dr. Verma integrates computational DFT analysis with AI- and ML-assisted materials modeling to accelerate the design and optimization of high-performance materials. His recent works include studies on dielectric and conductivity behavior of SrCeO₃, Ru-doped CNT/graphene-oxide supercapacitors, and MgAl₀.₅Fe₁.₅O₄ spinel ferrite systems. With over 20 scientific publications, an h-index of 6, and more than 90 citations, he has contributed significantly to understanding charge transport, dielectric relaxation, and structure–property relationships in multifunctional ceramics. His research aims to bridge experimental materials science and computational intelligence for developing sustainable, next-generation energy storage technologies and smart functional materials with enhanced performance and stability.

Profile : Google Scholar

Featured Publications

Verma, H., Tripathi, A., & Upadhyay, S. (2024). A comprehensive study of dielectric, modulus, impedance, and conductivity of SrCeO₃ synthesized by the combustion method. International Journal of Applied Ceramic Technology, 21(4), 3032–3047.

Verma, S., Das, T., Verma, S., Pandey, V. K., Pandey, S. K., Verma, H., & Verma, B. (2025). Hierarchically architecture of Ru-doped multichannel carbon nanotubes embedded with graphene oxide for supercapacitor material with long-term cyclic stability. Fuel, 381, 133517.

Verma, S., Maurya, A., Verma, H., Singh, R., & Bhoi, B. (2024). Unveiling the characteristics of MgAl₀.₅Fe₁.₅O₄ spinel ferrite: A study of structural, optical, and dielectric properties. Chemical Physics Impact, 9, 100674.

Nirala, G., Katheriya, T., Yadav, D., Verma, H., & Upadhyay, S. (2023). The evolution of coil-less inductive behaviour in La-doped Sr₂MnO₄. Emergent Materials, 6(6), 1951–1962.

Verma, H., Kumar, P., Satyarthi, S. K., Bhattacharya, B., Singh, A. K., & Upadhyay, S. (2025). Investigation of La₂FeO₄–rGO nanocomposite electrode material for symmetric and asymmetric supercapacitor. Journal of Energy Storage, 114, 115849.

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

Mr. Sandesh Aryal | Best Researcher Award

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Mr. Sandesh Aryal | Best Researcher Award

National Institute of Technology Rourkela | India

Mr. Sandesh Aryal holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology Rourkela (2021-2025) and has rapidly emerged as a research innovator in the domain of deep-learning–based biomedical image analysis. His work focuses on the classification of white blood cells via attention-augmented convolutional networks. His standout publication, “AFMNet: Adaptive Feature Modulation Network for Classification of White Blood Cells”, was published in Biocybernetics and Biomedical Engineering, and leverages adaptive spatial–channel feature modulation to achieve state-of-the-art classification performance. (Citation data: the article is indexed on ScienceDirect and ResearchGate.) His research interest spans machine learning, computer vision, biomedical signal processing, and image-based disease diagnostics. During his internship at Nepal Telecom, he gained practical exposure to wireless, transmission and power systems, complementing his core computational skills. His scholarship-supported undergraduate tenure and leadership roles (such as captain of his institute’s volleyball team) reflect a combination of technical excellence and organisational ability. With a proven ability to translate deep learning methods into biomedical applications, Sandesh is poised to contribute significantly to the intersection of AI and healthcare diagnostics.

Profile : Orcid

Featured Publication

Aryal, S., Naik, S. K., Madarapu, S., & Ari, S. (2025). AFMNet: Adaptive feature modulation network for classification of white blood cells. Biocybernetics and Biomedical Engineering.

Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

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Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

Samarkand State University | Uzbekistan

Dr. Fayzullo Nazarov is a distinguished researcher at Sharof Rashidov Samarkand State University, Uzbekistan, recognized for his contributions to artificial intelligence, data science, and computational optimization. With an h-index of 10, 28 publications, and 217 citations across 94 documents, his research demonstrates significant scholarly impact and growing recognition in the field. Dr. Nazarov earned his advanced degrees in computer science and applied mathematics, where he developed a strong foundation in algorithmic modeling, neural networks, and machine learning applications. His academic and professional experience centers on the intelligent management of data systems, optimization of distribution mechanisms, and neural network ensemble methodologies. Dr. Nazarov’s recent works, including studies on effective distribution determination using neural network ensembles and machine learning-based data storage optimization, highlight his innovative approach to integrating AI with intelligent system design. He actively collaborates with international researchers to advance computational intelligence and smart data technologies. Throughout his career, Dr. Nazarov has received multiple academic recognitions for excellence in research and publication. His dedication to advancing AI-driven optimization techniques positions him as a leading researcher in intelligent systems and computational innovation, with ongoing contributions to the digital transformation of data management and predictive modeling.

Profiles : Google Scholar | Scopus

Featured Publications

khatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Mechanisms of information reliability in big data and blockchain technologies. In Proceedings of the 2021 International Conference on Information Science and Communications.

Akhatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Increasing data reliability by using big data parallelization mechanisms. In Proceedings of the 2021 International Conference on Information Science and Communications.

Rashidov, A., Akhatov, A., & Nazarov, F. (2023). The same size distribution of data based on unsupervised clustering algorithms. In The International Conference on Artificial Intelligence and Logistics.

Akhatov, A. R., Sabharwal, M., Nazarov, F. M., & Rashidov, A. (2022). Application of cryptographic methods to blockchain technology to increase data reliability. In 2nd International Conference on Advance Computing and Innovative Technologies in Engineering.

Dagur, A., Shukla, D. K., Makhmadiyarovich, N. F., & Rustamovich, A. A. (2024). Artificial intelligence and information technologies: Proceedings of the 1st International Conference on Artificial Intelligence and Information Technologies (ICAIIT 2023). CRC Press.

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. Ateke Goshvarpour | Best Researcher Award

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Assoc. Prof. Dr. Ateke Goshvarpour | Best Researcher Award

Imam Reza International University | Iran

Ateke Goshvarpour is a distinguished researcher at Imam Reza International University, renowned for her extensive contributions to biomedical signal processing, emotion recognition, and neurophysiological data analysis. With an impressive h-index of 20, over 1,093 citations, and 70 research publications, her work has significantly advanced the integration of computational intelligence and physiological signal modeling. She has co-authored several influential studies on electroencephalography (EEG), electrocardiography (ECG), photoplethysmography (PPG), and galvanic skin response (GSR) for emotion and mental disorder recognition. Dr. Goshvarpour earned her higher education in biomedical engineering and has accumulated years of academic and research experience focusing on nonlinear analysis, chaos theory, and machine learning applications in healthcare. Her recent works explore quantum-inspired models, graph-based EEG analysis, and spectral–spatiotemporal fusion for diagnosing schizophrenia and cognitive disorders. She has been recognized for developing innovative feature fusion techniques that enhance accuracy in automated emotion recognition and neurodiagnostic systems. Her publications in high-impact journals such as Cognitive Neurodynamics, Chaos, Solitons & Fractals, and Biomedical Signal Processing and Control underscore her leadership in the field. Through her pioneering research, she continues to shape the future of computational neuroscience and affective computing, bridging the gap between biomedical engineering and mental health diagnostics.

Profiles : Scopus | Google Scholar

Featured Publications

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). An accurate emotion recognition system using ECG and GSR signals and matching pursuit method. Biomedical Journal, 40(6), 355–368. https://doi.org/10.1016/j.bj.2017.10.001

Goshvarpour, A., & Goshvarpour, A. (2019). EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences. Cognitive Neurodynamics, 13(2), 161–173. https://doi.org/10.1007/s11571-018-9510-8

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged Poincaré plots. Australasian Physical & Engineering Sciences in Medicine, 40(3), 617–629. https://doi.org/10.1007/s13246-017-0560-7

Goshvarpour, A., & Goshvarpour, A. (2020). Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Physical and Engineering Sciences in Medicine, 43(1), 227–238. https://doi.org/10.1007/s13246-019-00853-8

Goshvarpour, A., & Goshvarpour, A. (2018). Poincaré’s section analysis for PPG-based automatic emotion recognition. Chaos, Solitons & Fractals, 114, 400–407. https://doi.org/10.1016/j.chaos.2018.07.009

Dr. Sekhar Reddy Kola | Best Researcher Award

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Dr. Sekhar Reddy Kola | Best Researcher Award

National Yang Ming Chiao Tung University | Taiwan

Dr. Sekhar Reddy Kola is a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at National Yang Ming Chiao Tung University (NYCU), Taiwan. He earned his Ph.D. in Semiconductor Devices from NYCU, where he focused on the process variation effect and intrinsic parameter fluctuation of vertically stacked gate-all-around (GAA) silicon nanosheet complementary field-effect transistors (CFETs) under the supervision of Prof. Yiming Li. With a solid academic foundation, including an M.Sc. in Electronics and a B.S. in Mathematics, Electronics, and Computer Science from Sri Venkateswara University, India, Dr. Kola has made significant contributions to the field of semiconductor device physics and modeling. His research interests encompass GAA nanosheet and nanowire FETs, CFET design, statistical process variation modeling, reliability analysis, and machine learning applications in nanoelectronics. He has published 33 documents with over 311 citations and an h-index of 10, reflecting his impactful scientific contributions. Dr. Kola has received several honors, including the Best Paper Award at IEDMS 2018 and the Outstanding Foreign Student Scholarship from NYCU. Through his innovative research on nanoscale device modeling and variability analysis, Dr. Kola continues to advance the development of next-generation semiconductor technologies for sub-1-nm nodes.

Profiles : Google Scholar | Scopus | Orcid

Featured Publications

Kola, S. R., & Li, Y. (2025). Effects of bottom channel coverage ratio on leakage current and static power consumption of vertically stacked GAA Si NS FETs. ECS Journal of Solid State Science and Technology, 14(2), 025001.

Kola, S. R., Li, Y., & Butola, R. (2024). Statistical device simulation and machine learning of process variation effects of vertically stacked gate-all-around Si nanosheet CFETs. IEEE Transactions on Nanotechnology, 23, 386–392.

Kola, S. R., & Li, Y. (2023). Electrical characteristic and power fluctuations of GAA Si NS CFETs by simultaneously considering six process variation factors. IEEE Open Journal of Nanotechnology, 4, 112–120.

Sreenivasulu, V. B., Kumari, N. A., Kola, S. R., Singh, J., & Li, Y. (2023). Exploring the performance of 3-D nanosheet FET in inversion and junctionless modes: Device and circuit-level analysis and comparison. IEEE Access, 11, 42256–42265.

Kola, S. R., Li, Y., & Thoti, N. (2020). Effects of a dual spacer on electrical characteristics and random telegraph noise of gate-all-around silicon nanowire p-type MOSFETs. Japanese Journal of Applied Physics, 59(SGGA02).