Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

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Dr. Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Jinling Institute of Technology | China

Dr. Yuying Chen is a dedicated materials scientist in the Department of Materials Engineering at the School of Materials Engineering, Jinling Institute of Technology, Nanjing, China, where she contributes extensively to research, teaching, and the advancement of materials innovation. She earned her Ph.D. in Materials Science from the Harbin Institute of Technology and enriched her international academic profile through a visiting Ph.D. appointment at the Department of Mining and Materials Engineering at McGill University in Montreal, Canada. Her academic background also includes a Master’s degree in Materials Science from the Harbin Institute of Technology and a Bachelor’s degree in Metal Materials Engineering from Shenyang University of Technology. Dr. Chen’s research expertise encompasses first-principles calculations, hydrogen storage materials, interface engineering, alloying effects, metal hydrides, and computational modeling of welding processes. She has authored 8 documents that investigate hydrogen adsorption and desorption mechanisms, Mg/Ni and Mg/Ti interface stability, alkali- and alkaline-earth-metal-doped hydrides, Zn-induced embrittlement behavior in steels, and advanced modeling techniques for underwater wet welding and duplex stainless-steel welding under acoustic and vibrational fields. Her scholarly contributions have accumulated 90 citations and reflect an impactful research profile with an h-index of 5, demonstrating the academic significance and visibility of her work within the materials science community. Over the course of her academic journey, Dr. Chen has received numerous accolades, including Merit Student awards, multiple University Fellowships, Outstanding Student Leader recognition, and acknowledgment as an Excellent League Member at Harbin Institute of Technology. She has presented her research findings at major scientific gatherings, including the International Conference on Computational Design and Simulation of Materials and the Chinese Materials Conference. With a strong record in computational materials science and interface behavior, Dr. Chen continues to advance innovative methodologies and scientific understanding toward the design, optimization, and reliability of next-generation materials systems.

Profiles: Scopus | Orcid

Featured Publications

Chen, Y., Dai, J., & Song, Y. Catalytic mechanisms of TiH2 thin layer on dehydrogenation behavior of fluorite-type MgH2: A first principles study.

Chen, Y. Y., Dai, J. H., Xie, R. W., & Song, Y. A first-principles study on interaction of Mg/Ni interface and its hydrogen absorption characteristics.

Chen, Y. Y., Dai, J. H., Xie, R. W., Song, Y., & Bououdina, M. First principles study of dehydrogenation properties of alkali and alkali-earth metal doped Mg₇TiH₁₆.

Chen, Y. Y., Dai, J. H., & Song, Y. Stability and hydrogen adsorption properties of Mg/Mg₂Ni interface: A first principles study.

Dai, J. H., Chen, Y. Y., Xie, R. W., & Song, Y. Influence of alloying elements on the stability and dehydrogenation properties of Y(BH₄)₃ by first principles calculations.

Jitae Kim | Econometrics and Statistical Economics | Excellence in Research Award

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Dr. Jitae Kim | Econometrics and Statistical Economics | Excellence in Research Award

Environmental Planning Institute | South Korea

Dr. Jitae Kim is a distinguished environmental and resource economist serving as a Senior Researcher at the Environmental Planning Institute of Seoul National University and as an Academic Research Professor with the Korea Research Foundation. He holds a Ph.D., M.A., and B.A. in Economics, completing all degrees at leading Korean universities, and has built a multidisciplinary research portfolio spanning environmental planning, climate economics, applied econometrics, labor-market dynamics, and carbon policy analysis. His scholarly work covers meta-regression valuation of biodiversity, cost–benefit assessments of ecological projects, econometric forecasting of energy demand, and empirical investigations of how climate extremes such as heatwaves and typhoons influence labor markets, wages, and informal work conditions. He has contributed extensively to research on carbon mitigation under the Korean Emissions Trading Scheme, the co-benefits of green remodeling, and the transition toward sustainable energy systems. His international collaborations include major research projects on climate-disaster-driven labor-market disruptions in the Philippines, conducted with academic partners at Ateneo de Manila University. His publications appear in respected international and Korean journals, complemented by multiple working papers, conference presentations, and policy-oriented studies. Based on publicly available academic profiles, he has 2 indexed documents and approximately 109 citations, with an estimated h-index of about 1, reflecting the early but growing influence of his work. Before advancing into academia and research leadership, he served in analytical roles with institutions such as the Korea Capital Market Institute and the Korea Labor Institute, contributing to evidence-based policy development in environmental, labor, and economic sectors. Through his expanding body of research, Dr. Kim continues to shape discussions on just transition, climate-risk adaptation, sustainable energy planning, and equitable climate-finance allocation, establishing himself as a rising scholar dedicated to bridging empirical analysis with practical environmental policy solutions.

Profiles: Scopus | Google Scholar 

Featured Publication

Kim, J., Hong, J. H., & Kim, J. (2025). Energy consumption forecasting of neighborhood living facilities: A panel regression approach.

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

University of Lisbon | Portugal

Dr. Rasool Taban, Ph.D, is a distinguished Data Scientist currently affiliated with Technical University Institute – University of Lisbon, where he continues to advance the frontiers of Artificial Intelligence and Data Science. His academic journey began in Computer Engineering and evolved into a profound focus on Artificial Intelligence during his M.Sc. studies at the University of Tehran, where he graduated with honors in Artificial Intelligence and Robotics. His early research centered on developing an automated screening system designed to assist in diagnosing Autism Spectrum Disorder in children, demonstrating his ability to merge technology with meaningful social impact. Dr. Taban recently earned his Industrial Ph.D. at Institute – University of Lisbon, funded by the prestigious Marie Curie BIGMATH project, where his research specialized in addressing one of the most persistent challenges in statistical learning-imbalanced data. He successfully developed three novel balancing techniques, each tailored to optimize performance across different variable classes, making significant contributions to data reliability and analytical accuracy in machine learning models. With two published journal papers indexed in Scopus and SCI, Dr. Taban’s scholarly work reflects both academic rigor and applied innovation. He has also participated in multiple research and industry projects, collaborating with institutions such as the SDG Group, CIF/N26, Evenco International, and CTAD–Tehran Autism Center. His involvement as part of the editorial team for the International Conference on Robotics and Mechatronics (ICRoM) further underscores his leadership in advancing interdisciplinary research. Dr. Taban’s primary research interests include imbalanced data, statistical learning, data science, and financial data modeling. His contributions have not only expanded methodological knowledge in statistical computing but have also bridged the gap between theoretical frameworks and real-world data-driven applications, reflecting his commitment to excellence in both academia and industry.

Profiles:  Google Scholar | Linked In

Featured Publications

Taban, R., Nunes, C., & Oliveira, M. R. (2023). RM-SMOTE: A new robust balancing technique.

Taban, R., Nunes, C., & Oliveira, M. R. (2025). Mixed-robROSE: A novel balancing technique tailored for mixed-type datasets.

Bozorgnia, F., Arakelyan, A., & Taban, R. (2023). Graph-based semi-supervised learning for classification of imbalanced data. Submitted to Conference ENUMATH.

Shahri, M. A., & Taban, R. (2021). ML revolution in NLP: A review of machine learning techniques in natural language processing. Journal of Applied Intelligent Systems & Information Sciences (JAISIS), 2(1), 2.

Taban, R., Parsa, A., & Moradi, H. Tip-toe walking detection using CPG parameters from skeleton data gathered by Kinect. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 9).

Martin Ferrand| Statistical Modeling and Simulation | Best Researcher Award

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Dr. Martin Ferrand | Statistical Modeling and Simulation | Best Researcher Award

EDF R&D | France

Dr. Martin Ferrand is an accomplished researcher and engineer whose academic and professional journey has been defined by excellence in fluid dynamics, smoothed particle hydrodynamics (SPH), and computational modelling, and he is currently affiliated with the University of Manchester in the MACE Department, where he has undertaken advanced research as part of his MPhil studies, focusing on SPH with the specialized software SPARTACUS developed at EDF R&D; his academic foundations were laid at École des Ponts ParisTech, one of France’s most prestigious engineering institutions, where he specialized in mechanics, fluid mechanics, and data processing, complemented by intensive preparatory studies in advanced mathematics and physics at Lycée du Parc, Lyon, following his French Scientific Baccalaureate with distinction in 2004, which marked the start of a career shaped by intellectual rigor and scientific curiosity. Professionally, Ferrand has gained diverse and impactful experience, including at EDF R&D in Chatou, France, where he developed turbulence models incorporating buoyancy effects and enhanced the hydrodynamic reproduction of the Berre lagoon using TELEMAC3D, and at Imperial College London, where he expanded his expertise into stochastic processes by designing models to reproduce rainfall patterns for optimizing sewage infrastructure, while his immersion at Bouygues Construction gave him early exposure to industry practices, broadening his adaptability. His academic impact is evident through a strong research record comprising 70 scholarly documents, collectively cited 749 times, with an h-index of 11, reflecting the recognition, influence, and sustained relevance of his work in computational and environmental fluid mechanics; his expertise extends across theoretical, computational, and applied domains, establishing him as a versatile contributor to advancing both scientific understanding and engineering practice. In addition to his technical accomplishments, Ferrand is multilingual, fluent in French and English with working knowledge of German, enabling him to collaborate effectively in international research environments, while his interests outside academia, including football, cooking, and model train construction, showcase his creativity, discipline, and appreciation for teamwork, all of which complement his professional excellence. Overall, Martin Ferrand stands as a dedicated scholar and engineer whose combination of intellectual achievement, technical expertise, and international experience continues to make a significant contribution to his field and positions him as a rising figure in the global scientific community.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“Unified semi‐analytical wall boundary conditions for inviscid, laminar or turbulent flows in the meshless SPH method”

“Unified semi-analytical wall boundary conditions applied to 2-D incompressible SPH”

“An innovative method based on CFD to simulate the influence of photovoltaic panels on the microclimate in agrivoltaic conditions”

“A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods”

“Unsteady open boundaries for SPH using semi-analytical conditions and Riemann solver in 2D”