73 / 100

Assist Prof Dr. Amirah Alharthi, Statistics, Best Researcher Award.

Taif University, Saudi Arabia

Professional Profiles:

SCOPUSย 

ORCID

GOOGLE SCHOLAR

๐Ÿ‘จโ€๐ŸŽ“ Bio Summary:

Assistant Prof. Dr. Amirah Alharthi is a dedicated academic professional with a Ph.D. in Statistics from the University of Leeds, UK. She is currently serving as a Statistics Assistant Professor at Taif University, where she has excelled in teaching and research. Amirah has a strong background in mathematics and has contributed significantly to the field of statistics, particularly in statistical learning algorithms. Her work has been recognized with various honors and awards, and she has a notable publication record in reputable scientific journals. Amirah is fluent in Arabic and English, with basic proficiency in Spanish.

๐ŸŽ“ Education:

Amirah Alharthi holds a Ph.D. in Statistics from the University of Leeds, UK. Her doctoral research focused on statistical learning algorithms, culminating in a thesis titled “Weighted classification tree-based ensemble methods.” She also holds a Master’s degree and a Bachelor’s degree in Mathematics from Taif University, where she specialized in statistical inference.

๐Ÿ” Research Focus:

Amirah’s research focuses on statistical learning algorithms, with a particular interest in developing ensemble methods for classification tasks. Her work has contributed to the advancement of statistical inference and has practical applications in various fields, including finance and public health.

๐Ÿ† Honors & Awards:

Dr. Alharthi has received several honors and awards for her contributions to the field of statistics. She has been recognized for her excellence in teaching and research, as well as for her leadership roles within academic institutions.

Professional Experience: ๐Ÿ’ผ

Dr. Alharthi has a wealth of professional experience in academia and industry. She has served as a Statistics Assistant Professor at Taif University, teaching a variety of statistics and mathematics courses to students at different levels. Prior to her current role, she worked as a Mathematics Teacher for the Ministry of Education, where she honed her teaching skills and provided training for trainees.

๐Ÿ“š Top Noted Publications :

Estimation under a finite mixture of modified Weibull distributions based on censored data via EM algorithm with application
Authors: SF Ateya, AS Alharthi
Journal: J. Stat. Theory Appl.
Volume: 13
Issue: 3
Pages: 196-204
Year: 2014

Modified generalized Weibull distribution: theory and applications
Authors: MS Shama, AS Alharthi, FA Almulhim, AM Gemeay, MA Meraou, …
Journal: Scientific Reports
Volume: 13
Issue: 1
Pages: 12828
Year: 2023

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model
Authors: DM Khan, M Ali, N Iqbal, U Khalil, HM Aljohani, AS Alharthi, AZ Afify
Journal: Frontiers in Public Health
Volume: 10
Pages: 922795
Year: 2022

Estimations in a constant-stress partially accelerated life test for generalized Rayleigh distribution under Type-II hybrid censoring scheme
Authors: A Rabie, E Hussam, AH Muse, RA Aldallal, AS Alharthi, HM Aljohani
Journal: Journal of Mathematics
Year: 2022

Maximum likelihood estimation under a finite mixture of modified Weibull distributions based on censored data with application
Authors: SF Ateya, AS Alharthi
Journal: Journal of Applied Statistical Science
Volume: 20
Issue: 3
Pages: 33-41
Year: 2012

Author Metrics ๐Ÿ“Š :ย 

Dr. Alharthi’s publications have had a significant impact, with a strong citation record and high-quality contributions to the field of statistics. Her research is widely recognized for its innovative approach and practical relevance.

๐Ÿ“… Research Timeline:

Throughout her career, Amirah has demonstrated a continuous dedication to advancing the field of statistics. Her research timeline reflects a commitment to excellence and a passion for contributing to the scientific community.

Assist Prof Dr Amirah Alharthi | Statistics | Best Researcher Award |

You May Also Like