Dr. Samir K. Safi worked as a Professor of Statistics at the Islamic University of Gaza, Palestine (2014-2019), and in 2019, he moved to the United Arab Emirates University. He completed his Ph.D. studies in Statistics at the American University, Washington DC, USA. He has received multiple awards from the UAEU, including the Summer Undergraduate Research Experience in 2023 and 2024, and the College of Business and Economics Annual Research Program in 2022 and 2024. He also granted the Summer Research Collaboration Leave in 2021 and 2022. Additionally, he held a Departmental Instructorship at the American University in Washington, DC, from 2000 to 2004. Furthermore, he was supported by the Arab Student Aid International between 2001 and 2004 and the Karim Rida Said Foundation from 1997 to 1998. He recently led the Start-up project at UAE University in April 2022. He has published 50+ scientific research papers in various journals. Currently, he has been serving as a director of the Statistical Consulting Unit at UAEU since 2021. His teaching disciplines include Regression, Time Series, and Econometrics and his research interests include mixed data sampling (MIDAS) and investigating the efficiency of hybrid deep learning techniques, including Artificial Neural Networks (ANNs) models, compared to traditional forecasting methods for variables with different frequencies.

Sheema Gul, a Ph.D. candidate, has a strong academic background in statistics. She earned a B.Sc. in Double Math and Statistics (2018) and an M.Sc. in Statistics (Gold Medalist) (2020) from Abdul Wali Khan University Mardan. Since November 2023, she has been utilizing her expertise as a Research Assistant at the United Arab Emirates University. Her focus is on refining classification methods, particularly improving feature selection in binary class problems and exploring regularized tree forests for imbalanced classification. Prior to this role. She honed her research skills at Abdul Wali Khan University (KP, Pakistan) from November 2018 to June 2022. Her projects included tackling feature selection techniques for high-dimensional data, specifically gene expression problems, and investigating the impact of training data size on classification tasks. She actively participates in conferences, such as the 18th International Conference on Statistical Sciences (2021), where she showcased how data science tools can analyze gene expression data. Her passion lies in using cutting-edge techniques to solve real-world problems.