Prof. Gholamreza Hesamian, Payame Noor University, Iran
Dr. Gholamreza Hesamian ๐ is an accomplished Iranian statistician specializing in fuzzy statistical modeling and imprecise data analysis. Born on March 21, 1979, in Isfahan ๐ฎ๐ท, he currently serves at the Department of Statistics, Payame Noor University, Tehran ๐. With a Ph.D. in Statistics from Isfahan University of Technology, his work focuses on integrating fuzzy mathematics ๐ค into statistical inference. His research offers robust solutions in uncertain environments, making valuable contributions to modern statistical science ๐. Dr. Hesamian is dedicated to teaching, research, and academic development, helping shape the next generation of data scientists and statisticians ๐จโ๐ซโจ.
Profile :
๐Education & Experience :
Dr. Gholamreza Hesamian ๐ began his academic journey with a B.S. in Statistics from Isfahan University in 2000. He continued his studies at Isfahan University of Technology, earning an M.A. in Statistics ๐ in 2004, followed by a Ph.D. in Statistics ๐ in 2012. His doctoral research focused on Non-Parametric Statistical Inference based on Imprecise Information ๐, under the supervision of Dr. S.M. Taheri ๐จโ๐ซ. Currently, he serves as a faculty member at the Department of Statistics, Payame Noor University in Tehran ๐ข, where he teaches and conducts research in advanced statistical modeling and fuzzy mathematics.
๐ Professional Development :
Dr. Hesamian has consistently advanced his academic and professional development ๐ through dedicated research, publication, and active engagement in statistical education. He has participated in academic conferences ๐ค, contributed to scientific journals ๐, and collaborated on interdisciplinary projects involving fuzzy logic and probability ๐ค. His commitment to knowledge-sharing and curriculum innovation enhances statistical learning outcomes ๐ผ๐. Through workshops, seminars, and mentorship, he inspires students and young researchers to pursue excellence in data science, especially under conditions of uncertainty ๐ฏ๐ง . Dr. Hesamian remains a proactive contributor to Iranโs academic and research community in statistics ๐ฎ๐ท.
๐ฌ Research Focus :
Dr. Hesamianโs research focuses on fuzzy statistics ๐ค๐โa field that combines traditional statistical techniques with fuzzy logic to model uncertainty and imprecise information. His work enhances data analysis when classical probabilistic models fall short, particularly in decision-making environments marked by vagueness and ambiguity ๐ซ๏ธ๐. Key areas include fuzzy probability, fuzzy mathematical models, and non-parametric inference under uncertainty ๐. His research has wide applications in engineering, economics, and social sciences where exact data is difficult to obtain ๐งฎ๐ก. By bridging mathematics and real-world complexity, Dr. Hesamian contributes significantly to the development of intelligent and adaptive data systems ๐คโ๏ธ.
๐ Awards and Honors :
Dr. Gholamreza Hesamian ๐
has been recognized for his outstanding Ph.D. research in Non-Parametric Inference with Imprecise Data. He has received honors ๐๏ธ from Payame Noor University for Excellence in Research and has been commended ๐ for his significant academic contributions to Fuzzy Statistical Modeling. Additionally, he has actively participated ๐ in national conferences focused on Advanced Statistics and Fuzzy Systems, showcasing his dedication to advancing the field.
๐นPublication Top Notes :
1. A Fuzzy Multiple Regression Model Adopted with Locally Weighted and Interval-Valued Techniques
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Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova
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Journal: Journal of Computational and Applied Mathematics
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Year: 2026
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Type: Open Access
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Summary:
This study introduces a fuzzy multiple regression model that integrates locally weighted regression with interval-valued fuzzy techniques. The proposed method addresses uncertainties in predictor-response relationships and improves interpretability in fuzzy environments. Local weighting enables the model to adapt flexibly to localized data patterns, while interval-valued fuzzy numbers help handle imprecise or vague data.
2. A Two-way Crossed Effects Fuzzy Panel Linear Regression Model
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Authors: Gholamreza Hesamian, Arne Johannssen
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Journal: International Journal of Computational Intelligence Systems
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Year: 2025
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Volume: 18, Issue 1, Article 13
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Type: Open Access
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Summary:
This article proposes a fuzzy panel data regression model that incorporates two-way crossed random effects, capturing both individual and time-related variability. The fuzzy framework accommodates vagueness in longitudinal data, improving forecasting and inference in applications where uncertainty is prominent, such as economics and social sciences.
3. A Fuzzy Multivariate Regression Model to Control Outliers and Multicollinearity Based on Exact Predictors and Fuzzy Responses
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Authors: Gholamreza Hesamian, Mohammad Ghasem H. Akbari, Mehdi Shams
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Journal: Iranian Journal of Mathematical Sciences and Informatics
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Year: 2025
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Summary:
This model introduces a fuzzy multivariate regression approach designed to handle outliers and multicollinearity in regression analysis. It uses exact numerical predictors and fuzzy-valued responses to provide robust estimation and reduce the effect of anomalies or correlated variables, especially useful in uncertain data settings like finance or environmental studies.
4. A Flexible Soft Nonlinear Quantile-Based Regression Model
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Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova
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Journal: Fuzzy Optimization and Decision Making
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Year: 2025
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Type: Open Access
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Summary:
This article introduces a nonlinear soft regression model based on quantile estimation techniques in fuzzy environments. It allows modeling asymmetric distributions and tail behaviors under uncertainty. This is particularly useful in decision-making and risk assessment where traditional mean-based models fail to capture distributional extremes.
5. A Neural Network-Based ARMA Model for Fuzzy Time Series Data
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Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova
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Journal: Computational and Applied Mathematics
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Year: 2024
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Summary:
Combines ARMA (AutoRegressive Moving Average) models with neural networks for modeling fuzzy time series. This hybrid model handles both temporal dependencies and fuzzy uncertainty, offering improved accuracy in forecasting complex real-world systems such as energy demand or economic indicators.
๐นConclusion:
Given his trailblazing contributions to fuzzy statistical inference, commitment to academic excellence, and influence on the next generation of researchers, Dr. Gholamreza Hesamian embodies the values and vision of the Best Researcher Award. His work not only enhances statistical science but also provides vital tools for real-world decision-making under uncertainty. He is a deserving recipient of this recognition.