Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Published on by Statistics

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.

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

Published on by Statistics

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).