Dr. Mostafa Abbaszadeh is a distinguished scholar recognized for his significant contributions to computational mathematics and numerical analysis. He currently serves as Associate Professor in the Department of Mathematics and Computer Science at Amirkabir University of Technology in Tehran, Iran, where he has maintained continuous affiliation since 2014. His academic journey began with B.Sc and M.Sc degrees from Kashan University, completed in 2010 and 2012 respectively, followed by a PhD in Applied Mathematics from Amirkabir University of Technology in 2017. Dr. Abbaszadeh has established himself as a respected educator and researcher, mentoring numerous graduate students while developing sophisticated computational frameworks that bridge theoretical mathematics and practical applications across multiple scientific domains.
Dr. Abbaszadeh has achieved remarkable scholarly impact with a Scopus h-index of 42 and over 4,800 citations to his work, demonstrating substantial influence in the mathematical sciences community. His research program focuses on developing advanced numerical methods for solving complex differential equations including finite difference schemes for Schrodinger-Poisson equations with applications in optics and laser technology. He has made significant contributions to fractional integro-differential equations with time delays and pioneered machine learning approaches for financial prediction systems including credit scoring models and cryptocurrency price forecasting. His methodological innovations provide essential tools for scientists working in computational physics, financial mathematics, and engineering applications requiring precise numerical solutions.
Beyond his technical contributions, Dr. Abbaszadeh has supervised numerous MSc theses on cutting-edge topics ranging from machine learning applications to sophisticated numerical analysis techniques, often collaborating with prominent researchers including Mehdi Dehghan. His research group at Amirkabir University continues to advance the field through rigorous development of computational frameworks that address complex real-world problems. He maintains an active research agenda that integrates traditional numerical methods with contemporary machine learning approaches, ensuring continued relevance in evolving scientific landscapes. Dr. Abbaszadeh's work exemplifies the critical role of mathematical sciences in solving interdisciplinary challenges while maintaining the highest standards of analytical precision and methodological rigor.
