Abolfazl Saghafi

Abolfazl Saghafi is an Associate Professor in the Department of Mathematics at the University of South Florida, within the College of Arts and Sciences. He holds dual PhDs in Applied Mathematics from Iran University of Science and Technology and in Statistics from the University of South Florida.

Education:

• PhD in Statistics, University of South Florida
• PhD in Applied Mathematics, Iran University of Science & Technology
• M.Sc. in Mathematical Statistics, Shahid Beheshti University
• B.Sc. in Statistics, Azad University

His research interests are centered on machine learning, time series classification, BIG data analytics, parametric/non-parametric/Bayesian analysis, and extreme value probability distributions. His work bridges theoretical mathematics with practical applications, particularly in signal processing and data science. In 2011, he resolved a long-standing open problem on Poisson distributions of order k by providing a closed-form solution, marking a significant contribution to applied probability.

His recent publications focus on innovative applications of statistical and machine learning methods to real-world problems, particularly in object detection using ground-penetrating radar (GPR) data and improved parameter estimation in financial modeling. These works reflect a strong trend toward interdisciplinary research combining statistics, signal processing, and artificial intelligence.

Dr. Saghafi has collaborated with researchers such as S. Jazayeri, S. Esmaeili, and C.P. Tsokos, and holds a U.S. patent for buried object detection systems. His work has been published in journals such as Structural Control and Health Monitoring, Expert Systems with Applications, and Journal of Finance and Data Science.

Scientific Awards:

He advises students in statistics and applied mathematics, though specific names are not listed. He is affiliated with research teams working on data-driven modeling and signal analysis. His future work is likely to extend into deep learning applications for time series and robust statistical modeling under uncertainty.