Abazar Asghari | High Performance Computing | Best Researcher Award

Published on by Computer Science Engineering

Assoc. Prof. Dr . Abazar Asghari | High Performance Computing | Best Researcher Award

SCBFs Structures| University of Tehran |Iran

Dr. Abazar Asghari is an Associate Professor of Structural Engineering at the Faculty of Civil Engineering, Urmia University of Technology, Iran. He received his Ph.D. in Structural Engineering from Tehran University in 2002, focusing on the determination of ultimate loads and possible failure lines for continuous media using adaptive finite element methods. With more than three decades of academic, research, and technical experience, Dr. Asghari has made distinguished contributions to the areas of steel structure design, seismic performance evaluation, and nonlinear structural analysis. He has authored and co-authored over 25 scientific papers published in reputable international and national journals, including the Journal of Constructional Steel Research, Scientia Iranica, Neural Computing and Applications, and Structure and Infrastructure Engineering. His collaborative research with scholars such as Amir H. Gandomi and Saeed Saharkhizan has introduced innovative methodologies in seismic modeling, hybrid computational approaches, and performance-based structural design. Dr. Asghari is also the author of several highly regarded textbooks and design guides, including the multi-volume series Dynamics of Structures and Steel Structures Design, which are widely used in Iranian engineering education and practice. Beyond academia, he has played a major role in developing the Iranian National Building Code, serving as a technical committee member and primary text supplier for Chapter 10 on the design and construction of steel buildings. He currently chairs the Sub-Committee on Loads and Pressures (ISIRI/TC98/SC2) at the National Iranian Standards Organization and serves on the Board of Directors of the Iranian Society of Steel and Structures. Through his extensive research, teaching, and standardization work, Dr. Asghari has significantly contributed to advancing structural safety, seismic resilience, and sustainable engineering practices both in Iran and internationally.

Featured Publication

Gandomi, A. H., Faramarzifar, A., Rezaee, P. G., Asghari, A., & Talatahari, S. (2015). New design equations for elastic modulus of concrete using multi expression programming. Journal of Civil Engineering and Management, 21(6), 761–774. Cited by: 83

Asghari, A., & Saharkhizan, S. (2019). Seismic design and performance evaluation of steel frames with knee-element connections. Journal of Constructional Steel Research, 154, 161–176. Cited by: 49

Asghari, A., & Gandomi, A. H. (2016). Ductility reduction factor and collapse mechanism evaluation of a new steel knee braced frame. Structure and Infrastructure Engineering, 12(2), 239–255. Cited by: 33

Aminian, P., Javid, M. R., Asghari, A., Gandomi, A. H., & Esmaeili, M. A. (2011). A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method. Neural Computing and Applications, 20(8), 1321–1332. Cited by: 32

Jaberi, V., & Asghari, A. (2022). Seismic behavior of linked column system as a steel lateral force resisting system. Journal of Constructional Steel Research, 196, 107428. Cited by: 26

Samira Rahim | Computational Finance | Best Researcher Award

Published on by Computer Science Engineering

Assist. Prof. Dr. Samira Rahim | Computational Finance | Best Researcher Award

Assist. Prof. Dr | University of Sciences and Technology Houari Boumediene | Algeria

Samira Rahim specializes in the field of stochastic methods, statistical modeling, and time series analysis, with a strong focus on the development and application of advanced probabilistic techniques for real-world data interpretation. Her research explores complex stochastic models, particularly in financial and meteorological contexts, where uncertainty and periodicity play critical roles. Through her work on periodic GARCH and threshold GARCH models, Rahim has contributed to improving the understanding of volatility dynamics and the statistical behavior of financial series with cyclical characteristics. Her investigations emphasize the importance of model mixtures and the application of local asymptotic normality principles, offering deeper insights into how stochastic processes can better capture temporal dependencies in financial and environmental data. With a background in statistics and operational research, Rahim integrates both theoretical and applied perspectives to construct robust predictive and inferential frameworks. Her expertise extends to stochastic optimization and probabilistic analysis, contributing to the development of more adaptive forecasting tools for risk management and decision-making under uncertainty. Her academic contributions, including her publication in Communications in Statistics – Theory and Methods in 2025, demonstrate her commitment to advancing research in the mathematical and statistical sciences. Rahim’s long-term experience in academia and her engagement with research projects underscore her dedication to innovation in stochastic modeling and data-driven approaches. Her work aligns with contemporary challenges in data science, emphasizing the critical role of statistical methodologies in improving predictive accuracy and understanding complex systems governed by random processes. Samira Rahim’s research continues to strengthen the intersection of probability theory, operational research, and applied statistics, contributing significantly to both theoretical advancements and their practical implementations in diverse scientific domains.

Profiles: Google Scholar
Featured Publication

Rahim, S., & Guerbyenne, H. (2025). Local asymptotic normality in periodic threshold GARCH and periodic GARCH models. Communications in Statistics – Theory and Methods, 1–20.