معرفی
Farhad Shokoohi is an Assistant Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on statistical modeling, machine learning, and their applications in healthcare analytics, bioinformatics, and financial forecasting. He has contributed to advancements in Bayesian methods, small area estimation, and computational statistics, with particular emphasis on cancer genomics and epigenetic analysis.
Key research interests include hybrid statistical models (e.g., SP-RF-ARIMA for energy load forecasting), hidden Markov models for genomic data analysis, and sparse estimation techniques in high-dimensional data. His work bridges theoretical statistics with practical applications in finance, healthcare, and policy evaluation. He has developed R packages like fmrs for finite mixture models, emphasizing computational tools for statistical inference.
His articles reflect a blend of methodological innovation and domain-specific solutions, spanning topics such as differential methylation in colorectal cancer, dual-input deep learning for forex prediction, and trans-dimensional MCMC for genomic studies. While no awards or grants are explicitly listed in the provided texts, his interdisciplinary approach underscores contributions to both computational and applied statistical research.
