Department of Statistics at Shahid Bahonar University of Kerman, Iran.
Ayyub Sheikhi possesses a robust set of research skills honed through years of academic and professional experience. Proficient in both theoretical and applied aspects of statistics and data science, he excels in experimental design, statistical analysis, and modeling techniques. Ayyub demonstrates proficiency in various statistical software packages, including R, and is adept at implementing advanced methodologies such as machine learning algorithms and Bayesian inference. His research skills also extend to data visualization and interpretation, allowing him to effectively communicate complex statistical findings to diverse audiences. Ayyub’s expertise in statistical research methodologies is further augmented by his ability to collaborate across interdisciplinary teams and lead projects from conception to completion. With a keen attention to detail and a commitment to methodological rigor, Ayyub Sheikhi continues to make significant contributions to the advancement of statistical science.
Professional Profiles:
Education
Ayyub Sheikhi pursued his academic journey in statistics and applied mathematics, earning his Ph.D. in Applied Mathematics from Shahid Bahonar University of Kerman, Iran, in 2012. Prior to that, he completed his Master of Science in Statistics at AmirKabir University of Technology, Tehran, Iran, and his Bachelor of Science in Statistics at Shahid Bahonar University of Kerman. His educational background equipped him with a strong foundation in mathematical and statistical principles, which he has since applied in both academic and professional settings, contributing significantly to the field of statistics and data science.
Professional Experience
Ayyub Sheikhi has garnered extensive professional experience in academia, research, and leadership roles. From 2018 to 2020, he served as the Head of the Department of Statistics at Shahid Bahonar University of Kerman, Iran, where he demonstrated his leadership skills in managing departmental affairs and fostering academic excellence. Concurrently, since 2015, he has held the position of Assistant Professor in the Department of Statistics at the same university, contributing to both teaching and research activities. In 2020, he was promoted to the position of Associate Professor, recognizing his scholarly achievements and expertise in the field. Additionally, since 2021, Ayyub has taken on the role of Head of the “Data Mining and Machine Learning” group at the Mahani Mathematical Research Institute, Shahid Bahonar University of Kerman, further showcasing his commitment to advancing statistical research and methodologies. Moreover, he has enriched his professional experience through a research fellowship at the University of Plymouth, UK, during the academic year 2023-2024, broadening his perspective and collaborating with international scholars in the field of statistics and data science.
Research Interest
Ayyub Sheikhi’s research interests encompass a diverse array of topics within the field of statistics and data science. He is particularly interested in Bayesian methods, change-point detection, and dynamic time series analysis, exploring innovative approaches to model complex data structures and uncover temporal patterns. Additionally, he focuses on copula-related techniques for analyzing dependence structures in multivariate data, aiming to develop robust methodologies for financial modeling and risk management. Ayyub also investigates machine learning methods, such as clustering algorithms and support vector machines, with a specific focus on their applications in outlier detection, classification, and regression tasks. Furthermore, he is keen on exploring the intersection of statistics with other disciplines, including economics, medicine, and engineering, to address real-world challenges and advance interdisciplinary research initiatives. Overall, Ayyub’s research interests reflect his dedication to advancing statistical theory and methodology while addressing practical problems across various domains.
Award and Honors
Ayyub Sheikhi, a prominent figure in the field of statistics and data science, has accumulated a wealth of experience and expertise over the years. He has held various academic and research positions, including serving as the Head of the Department of Statistics at Shahid Bahonar University of Kerman, Iran, from 2018 to 2020, and as an Assistant Professor and later an Associate Professor in the same department. Ayyub’s academic journey began with a Ph.D. in Applied Mathematics from Shahid Bahonar University of Kerman, Iran, in 2012, complemented by an M.Sc. in Statistics from AmirKabir University of Technology, Tehran, and a B.Sc. in Statistics from Shahid Bahonar University of Kerman. His scholarly contributions extend beyond academia, as evidenced by his involvement in various visiting appointments, publication of books, development of statistical packages, and leadership in research projects. Ayyub’s commitment to excellence and innovation in statistics continues to drive advancements in the field, positioning him as a respected authority and mentor within the academic community.
Research Skills
Dr. Sheikhi Ayyub possesses a breadth of research skills spanning statistical and mathematical domains, underpinned by a dedication to practical problem-solving. His expertise encompasses statistical inference, where he adeptly employs hypothesis testing, estimation, and model fitting to derive meaningful insights from data. Proficient in data analysis techniques, Dr. Ayyub excels in uncovering patterns and trends within complex datasets, thereby facilitating informed decision-making processes. Moreover, he demonstrates a keen understanding of machine learning algorithms, employing them for tasks ranging from classification and regression to clustering and anomaly detection, thereby enhancing predictive modeling capabilities. Additionally, Dr. Ayyub showcases proficiency in time series analysis, employing various techniques to analyze temporal data, identify patterns, and forecast future values effectively. Furthermore, his expertise extends to Bayesian statistics, copula models, and robust statistical methods, further enriching his analytical toolkit. Proficient in utilizing statistical software packages like R, Python, and MATLAB, Dr. Ayyub applies these skills across interdisciplinary research endeavors, contributing to advancements in fields ranging from healthcare and economics to engineering and finance.
Publications
- A two-stage Bridge estimator for regression models with endogeneity based on control function method
- Authors: Bahador, F., Sheikhi, A., Arabpour, A.
- Year: 2024
- Journal: Computational Statistics
- Volume: 39
- Issue: 3
- Pages: 1351–1370
- Citations: 0
- Convex weak concordance measures and their constructions
- Authors: Mesiar, R., Kolesárová, A., Sheikhi, A., Shvydka, S.
- Year: 2024
- Journal: Fuzzy Sets and Systems
- Volume: 478
- Pages: 108841
- Citations: 1
- Multivariate Asymmetric Distributions of Copula Related Random Variables
- Authors: Sheikhi, A., Arad, F., Mesiar, R.
- Year: 2023
- Journal: Austrian Journal of Statistics
- Volume: 52
- Issue: 4
- Pages: 1–14
- Citations: 0
- A dimension reduction in neural network using copula matrix
- Authors: Sheikhi, A., Mesiar, R., Holeňa, M.
- Year: 2023
- Journal: International Journal of General Systems
- Volume: 52
- Issue: 2
- Pages: 131–146
- Citations: 3
- Convex concordance measures
- Authors: Mesiar, R., Kolesárová, A., Sheikhi, A.
- Year: 2022
- Journal: Fuzzy Sets and Systems
- Volume: 441
- Pages: 366–377
- Citations: 2
- Copula-based Berkson measurement error models
- Authors: Ziaei, A.R., Zare, K., Sheikhi, A.
- Year: 2022
- Journal: Iranian Journal of Fuzzy Systems
- Volume: 19
- Issue: 4
- Pages: 165–175
- Citations: 0
- A heteroscedasticity diagnostic of a regression analysis with copula dependent random variables
- Authors: Sheikhi, A., Arad, F., Mesiar, R.
- Year: 2022
- Journal: Brazilian Journal of Probability and Statistics
- Volume: 36
- Issue: 2
- Pages: 408–419
- Citations: 0
- ON ASYMMETRIC DISTRIBUTIONS OF COPULA RELATED RANDOM VARIABLES WHICH INCLUDES THE SKEW-NORMAL ONES
- Authors: Sheikhi, A., Arad, F., Mesiar, R.
- Year: 2022
- Journal: Kybernetika
- Volume: 58
- Issue: 6
- Pages: 984–995
- Citations: 1
- Asymmetric distributions based on the t-copula
- Authors: Arad, F., Sheikhi, A.
- Year: 2022
- Conference: 9th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2022
- Citations: 0
- On a generalization of the test of endogeneity in a two stage least squares estimation
- Authors: Sheikhi, A., Bahador, F., Arashi, M.
- Year: 2022
- Journal: Journal of Applied Statistics
- Volume: 49
- Issue: 3
- Pages: 709–721
- Citations: 11