Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

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Dr. Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

Korean National Institute for International Education | South Korea

Dr. Vikas Mehta is a structural engineer and researcher specializing in seismic performance optimization, sustainable construction materials, and the application of advanced computational and machine learning methodologies to civil infrastructure systems. He completed his Ph.D. in Civil Engineering at Keimyung University, South Korea, where his award-winning doctoral research introduced innovative modifier-based and data-driven techniques for improving shear strength prediction and design accuracy in reinforced concrete beam-column joints. His expertise spans nonlinear finite element modeling, fragility analysis, physics-informed and graph-based machine learning, geospatial analytics, and performance-based seismic assessment, supported by strong proficiency in ETABS, OpenSees, SeismoSoft, Abaqus, MATLAB, Q-GIS, SPSS, Python, PyTorch, WEKA, and OriginPro. Dr. Mehta serves as a Postdoctoral Researcher at the Chonnam National University R&BD Foundation, contributing to advanced safety technologies for nuclear power plant structures under extreme hazard scenarios, including buckling resistance enhancement, retrofit optimization, and complex wind–terrain interaction studies. His professional background includes academic appointments in structural and construction engineering, where he taught subjects in earthquake engineering, finite element analysis, and structural systems while supervising graduate research and contributing to curriculum and laboratory development. Dr. Mehta has authored a substantial body of SCI-indexed research on seismic damage prediction, torsional behavior modeling, hybrid AI-mechanics frameworks, recycled and sustainable materials, computational methods, and structural performance evaluation, complemented by multiple patents in construction materials, damping devices, and waste-based composites. He has presented at leading international and national conferences and contributed to funded collaborative research, including projects involving global academic and industry partners. His professional affiliations include membership in ASCE, the Institute of Physics (AMInstP), IAEME (Fellow), and licensure as a Class-A engineer under the Himachal Pradesh Town and Country Planning Act. Dr. Mehta’s contributions to structural engineering and computational mechanics continue to gain international visibility, reflected in an h-index of 7, over 172 citations, and more than 19 published documents, underscoring his growing influence in machine learning–driven structural design, seismic resilience, and sustainable construction innovation.

Profiles: Scopus | Orcid

Featured Publications

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Corrigendum to “Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints”.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Predictive framework for shear strength and failure modes of exterior reinforced concrete beam–column joints using machine learning. Structural Concrete. h.

Sagar, G. S., Mukthi, S., & Mehta, V. (2025). Analyzing compressive, flexural, and tensile strength of concrete incorporating used foundry sand: Experimental and machine learning insights. Archives of Computational Methods in Engineering.

Mehta, V., Thakur, M. S., & Chey, M. H. (2025). Enhancing seismic design accuracy of RC beam-column joints: Modifier-based approach for shear strength predictions. Structures.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints. Structures.

Seyed Abolfazl Hosseini | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Seyed Abolfazl Hosseini | Statistical Modeling and Simulation | Best Researcher Award

Dr. Seyed Abolfazl Hosseini | Islamic Azad University | Iran

Dr. Seyed Abolfazl Hosseini is an accomplished electrical engineer and academic whose work seamlessly integrates communications systems, signal processing, machine learning, and remote sensing. He earned his Ph.D. in Communications Systems Engineering from Tarbiat Modares University, following an M.Sc. from K. N. Toosi University and a B.Sc. in Control Engineering from Sharif University of Technology. Over his academic career, he has held leadership roles including Dean of the Electrical & Electronics Research Centre, head of the Communications Engineering Department, and overseen more than 35 M.Sc. theses and 5 Ph.D. dissertations. According to his publication record encompasses more than 5 documents, and his works have been cited over 18 times, with an h-index of 3. He has published in top journals on topics such as MIMO-UFMC system optimization, hyperspectral image classification, blind watermarking, and nonparametric density estimation. Beyond research, he has directed industry projects in IoT, AI, surveillance, and power systems, and contributed to drafting technical standards for electricity markets. Dr. Hosseini is proficient in MATLAB, Python, and advanced mathematics including stochastic processes, linear algebra, fractal theory, and graph theory. He continues to blend theory with practice, driving innovation and teaching the next generation of engineers.

Profiles: Scopus | Orcid | ResearchGate

Featured Publications

Hassan Abdollahpour, H., Hosseini, S. A., Raeisi, N., & Azam, F. 3D geometry modeling method for MIMO communication systems using correlation coefficients. Journal of Computer Networks and Communications.

Aghamiri, H. R., Hosseini, S. A., Green, J. R., & Oommen, B. J. Nonparametric probability density function estimation using the Padé approximation. Algorithms.

Asgharnia, M., Hosseini, S. A., Shahzadi, A., Ghazi-Maghrebi, S., & Shaghaghi Kandovan, R. Optimization framework for user clustering, beamforming design and power allocation in MIMO-UFMC systems. IEEE Access.

Khalili, F., Razzazi, F., & Hosseini, S. A. Registration of remote sensing images by the combination of complex nonlinear diffusion and phase congruency attributes. Journal of the Indian Society of Remote Sensing.

Hosseini, S. A., et al. A simple method to prepare and characterize optical fork-shaped diffraction gratings for generation of orbital angular momentum beams. Journal of Optics.

Haifa Jammeli | Operations Research and Statistical Optimization | Best Researcher Award

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Dr. Haifa Jammeli | Operations Research and Statistical Optimization | Best Researcher Award

Normasys | France

Dr. Haifa Jammeli is a research fellow at the Institut Supérieur de Gestion de Tunis, specializing in business computing and operations research. She holds a PhD from the Higher Institute of Management in Tunisia and a Master’s degree in Logistics and Transportation Sciences from the Higher Institute of Logistics and Transportation, Sousse University. Her academic and professional journey spans over a decade, with significant contributions to supply chain optimization, AI in logistics, and sustainable urban planning. She is a part-time instructor at Paris Nanterre University and NEOMA Business School, teaching courses in supply chain management, operations research, data analysis, and IT project management. Her research focuses on optimizing transportation routes for COVID-19 patients and cash logistics using tools like CPLEX, QGIS, and Python, and she has developed AI models to forecast urban solid waste generation and propose green logistics solutions for household waste collection. With over 70 citations across nine publications, her work has been presented at international conferences and published in journals such as IEEE Transactions on Engineering Management and Annals of Operations Research. Her h-index is 10, reflecting both productivity and impact in her field. She has received awards including the Perficio Award for Best Woman Entrepreneur of the Year and was a finalist for the IFROS Prize for Operational Research in Development. Fluent in English, French, and Arabic, she combines strong technical skills (CPLEX, MATLAB, Python, R, SQL, QGIS) with experience in teaching, project leadership, and applying AI-based decision models to real‐world sustainability challenges.

Profiles: Scopus | Orcid

Featured Publications

Alaya, H., Jammeli, H., Ben Abdelaziz, F., Masmoudi, M., & Verny, J. (2024). Sustainable logistics for transfer of COVID-19 patients: Lesson learned from France. International Transactions in Operational Research.

Jammeli, H., & Verny, J. (2024). A multi-objective model for two-level distribution system in the city of Paris. Annals of Operations Research. (Accepted for publication)

Jammeli, H., Khefacha, A., Sellei, B., & Verny, J. (2023, October 18–21). The impact of AI tools in education environment. In 2023 IEEE ASEE Frontiers in Education Conference, College Station, Texas.

Jammeli, H., Alaya, H., & Verny, J. (2023, October 23–25). An analysis of the role of the Internet of Things and sensor technologies in optimizing waste management in the city of Sousse, Tunisia . World Recycling Convention, Madrid, Spain.

Jammeli, H., & Verny, J. (2022). A literature review for green smart home delivery problem in urban environments. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 756–760). IEEE.

Saikat Biswas | Operations Research and Statistical Optimization | Best Researcher Award

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Dr. Saikat Biswas | Operations Research and Statistical Optimization | Best Researcher Award

IIT Roorkee | India

Dr. Saikat Biswas is an Indian chemical engineer and academic whose research primarily focuses on computational fluid dynamics (CFD), multiphase flow, and microfluidics, with a special emphasis on droplet dynamics including breakup, splitting, and the transition from dripping to jetting in complex microchannel geometries. He earned his PhD in Chemical Engineering from the Indian Institute of Technology Guwahati (2016–2023), where his doctoral work investigated droplet breakup dynamics in confined microscale flows, and previously completed both his M.Tech and B.Tech in Chemical Engineering at the National Institute of Technology Agartala. Throughout his academic journey, he has published 14 documents, accumulating 41 citations and achieving an h-index of 3, reflecting his growing impact in the field. His contributions include both numerical and computational studies, such as two-dimensional and three-dimensional simulations of droplet splitting at T-junctions and multifurcating channels, investigations of flow-focusing geometries, and analyses of the role of viscosity ratio, surface tension, and channel design in influencing microfluidic droplet behaviour. Skilled in advanced tools such as ANSYS Fluent, COMSOL Multiphysics, OpenFOAM, and MATLAB, he integrates computational methods with engineering applications to address fundamental and applied challenges. Recognized as hard-working, adaptable, and collaborative, Biswas continues to contribute to the advancement of microfluidics and multiphase flow research.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“Digital electronic based portable device for colorimetric quantification of ketones and glucose level in human urine”

“Droplet splitting in multifurcating microchannel: A three-dimensional numerical simulation study”

“3D simulation of dripping and jetting phenomena in a flow-focusing geometry”

“A computational study on transition mechanism of dripping to jetting flow in a flow focusing geometry”

“Influence of microchannel geometry on droplet breakup dynamics: A computational study”