Abolfazl Saghafi earned his first Ph.D. in Applied Mathematics from Iran University of Science and Technology in 2012 where he specialized in divergence measures, entropy, Weibull, and order k Poisson models. Driven by a passion for advanced analytics, he earned his second Ph.D. in Statistics from University of South Florida in 2017, specializing in machine learning, time series classification and BIG data analytics.
Since 2022, Dr. Saghafi has served as an Associate Professor of Statistics and Data Science at Saint Joseph’s University. His academic and research interests lie at the intersection of statistical modeling and modern computational techniques. His current work emphasizes machine learning, deep learning, time series classification, and the development of robust statistical methodologies for high-dimensional data.
Dr. Saghafi has a strong record of scholarly productivity, with 17 peer-reviewed journal articles and 3 patents granted by the United States Patent and Trademark Office (USPTO). His research not only advances theoretical understanding but also contributes practical solutions to complex data-driven challenges across various scientific and industrial domains.
Abolfazl Saghafi earned his first Ph.D. in Applied Mathematics from Iran University of Science and Technology in 2012 where he specialized in divergence measures, entropy, Weibull, and order k Poisson models. Driven by a passion for advanced analytics, he earned his second Ph.D. in Statistics from University of South Florida in 2017, specializing in machine learning, time series classification and BIG data analytics.
Since 2022, Dr. Saghafi has served as an Associate Professor of Statistics and Data Science at Saint Joseph’s University. His academic and research interests lie at the intersection of statistical modeling and modern computational techniques. His current work emphasizes machine learning, deep learning, time series classification, and the development of robust statistical methodologies for high-dimensional data.
Dr. Saghafi has a strong record of scholarly productivity, with 17 peer-reviewed journal articles and 3 patents granted by the United States Patent and Trademark Office (USPTO). His research not only advances theoretical understanding but also contributes practical solutions to complex data-driven challenges across various scientific and industrial domains.