Ali Tarkashvand | Mathematics | Best Researcher Award

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Best Researcher Award

Ali Tarkashvand
Iran University of Science and Technology

Ali Tarkashvand
Affiliation Iran University of Science and Technology
Country Iran
Scopus ID 57053412400
Documents 28
Citations 381
h-index 13
Subject Area Mathematics
Event International AI Data Scientists Award
ORCID 0000-0002-7464-5501

Ali Tarkashvand of Iran University of Science and Technology has established a research profile in mathematics through peer-reviewed publications, scholarly collaborations, and citation performance. His academic record reflects continued engagement with mathematical research and knowledge dissemination within the scientific community.[1]

Abstract

This article presents a concise academic overview of Ali Tarkashvand, highlighting scholarly productivity, citation influence, and contributions to mathematical research. The profile is prepared in the context of recognition through the Best Researcher Award and summarizes available research indicators and professional achievements.[1]

Keywords

Mathematics, Research Excellence, Scholarly Impact, Citations, Academic Achievement, Scientific Publications, Best Researcher Award.

Introduction

Recognition programs in academia often assess publication quality, citation performance, and contributions to knowledge creation. Ali Tarkashvand’s scholarly activities demonstrate active participation in mathematical research and academic dissemination through indexed publications and collaborative studies.[2]

Research Profile

Based on available indexing data, the researcher has authored 28 scholarly documents and accumulated 381 citations, resulting in an h-index of 13. These indicators suggest a consistent record of publication and academic visibility within relevant research domains.[1]

Research Contributions

The research contributions of Ali Tarkashvand are associated with mathematical investigations, analytical methodologies, and scholarly collaboration. His publications contribute to the broader understanding of mathematical theory and its applications across interdisciplinary environments.[3]

Publications

  • Indexed peer-reviewed mathematics publications.
  • Research articles appearing in international scholarly journals.
  • Collaborative studies contributing to mathematical knowledge development.

Research Impact

Citation metrics indicate that the researcher’s work has received scholarly attention from the academic community. The accumulated citation count reflects engagement by other researchers and demonstrates measurable influence within the field.[1]

Award Suitability

The Best Researcher Award emphasizes excellence in research productivity, scholarly impact, and professional contribution. Considering the publication record, citation metrics, and academic engagement of Ali Tarkashvand, the profile aligns with key evaluation criteria commonly applied in research recognition programs.[4]

Conclusion

Ali Tarkashvand has established a recognized research profile supported by peer-reviewed publications, citation performance, and ongoing scholarly engagement. These achievements illustrate a commitment to advancing mathematical research and support consideration for academic recognition through the Best Researcher Award.

References

  1. Elsevier. (n.d.). Scopus author details: Ali Tarkashvand, Author ID 57053412400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57053412400
  2. Google Scholar. (n.d.). Scholar profile and citation overview.
    https://scholar.google.com/citations?user=Xrbap3EAAAAJ&hl=en&oi=ao
  3. DOI Foundation. (n.d.). Digital Object Identifier reference resource.
    https://doi.org/10.1016/j.matcom.2020.01.001
  4. International AI Data Scientists Award. (n.d.). Award evaluation and recognition framework.
    https://aidatascientists.com/

Nikolaos Gkrekas | Mathematics | Best Researcher Award

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Mr. Nikolaos Gkrekas | Mathematics | Best Researcher Award

Researcher at University of Kansas | United States

Mr. Nikolaos Gkrekas is a mathematician whose research and academic contributions bridge the domains of dynamical systems, mathematical modeling, and applied analysis. His scholarly work demonstrates an interdisciplinary approach, uniting mathematical theory with practical applications in science, engineering, and education. He has authored several peer-reviewed papers in internationally recognized journals, addressing complex phenomena such as chaos, quasi-geostrophic equations, and epidemiological models, while also exploring the evolving role of artificial intelligence in mathematics education and research. His research reveals a consistent focus on nonlinear dynamics, mathematical modeling, and the interplay between theoretical structures and real-world systems. In addition to his research, he has participated in numerous international conferences, seminars, and workshops hosted by institutions such as Harvard University, Kyoto University, and the University of Essex, reflecting his active engagement with the global mathematics community. His involvement as a peer reviewer for top-ranked journals, including Chaos, Solitons & Fractals and Nonlinear Engineering, alongside his editorial role in applied mathematics publications, underscores his academic credibility and contribution to maintaining high standards in scientific communication. Nikolaos is also affiliated with prominent mathematical societies and research groups, emphasizing his dedication to advancing mathematical sciences and fostering collaborative research. His intellectual versatility, combined with his passion for analytical problem-solving, positions him as an emerging figure in modern mathematical research, recognized for integrating rigorous mathematical theory with insightful applications to complex systems and education.

Profile: Google Scholar

Featured Publications

Rizos, I., & Gkrekas, N. (2023). Incorporating history of mathematics in open-ended problem solving: An empirical study.

Rizos, I., & Gkrekas, N. (2022). Teaching and learning sciences within the COVID-19 pandemic era in a Greek university department.

Rizos, I., & Gkrekas, N. (2023). Is there room for conjectures in mathematics? The role of dynamic geometry environments.

Gkrekas, N. (2024). Applying Laplace transformation on epidemiological models as Caputo derivatives.

Rizos, I., & Gkrekas, N. (2022). The historical background of a famous indeterminate problem and some teaching perspectives.

Maedeh GholamAzad | Mathematics | Best Researcher Award

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Dr. Maedeh GholamAzad | Mathematics | Best Researcher Award

Postdoctoral Researcher at University of Kurdistan, Iran

Dr. Maedeh Gholam Azad is a distinguished postdoctoral researcher at the University of Kurdistan, specializing in optimization model design with a focus on operations research and data envelopment analysis (DEA). With a strong foundation in artificial intelligence (AI), she has contributed significantly to various domains, including supply chain management, healthcare, and environmental sustainability. Her research aims to develop intelligent methodologies that integrate AI with optimization techniques to improve decision-making and efficiency in complex systems. Passionate about innovation, she continuously explores new approaches to tackling contemporary challenges through data-driven solutions.

Profile

Scopus

Education

Dr. Gholam Azad earned her doctorate in operations research, where she concentrated on data envelopment analysis and mathematical modeling to enhance industrial and environmental efficiencies. Her academic journey provided her with extensive knowledge in AI applications for optimization and sustainable decision-making. Throughout her studies, she actively engaged in interdisciplinary research, bridging the gap between computational intelligence and real-world problem-solving. Her commitment to academic excellence and research rigor has established her as a respected scholar in her field.

Experience

With extensive experience in research and academia, Dr. Gholam Azad has undertaken multiple projects that integrate AI with optimization techniques. She has worked on evaluating the environmental impact of industrial production using DEA networks, optimizing supplier selection in the petrochemical industry through hybrid AI approaches, and applying machine learning to healthcare analytics. Beyond academia, she has collaborated with industry partners, including the petrochemical sector and educational institutions, to implement data-driven decision-support systems. Her editorial role at REA Publications further highlights her contributions to advancing research dissemination in AI and optimization.

Research Interests

Dr. Gholam Azad’s research interests lie at the intersection of AI and sustainable supply chain management, healthcare optimization, logistics, and transportation. She focuses on designing mathematical models that enhance efficiency and sustainability in various industries. Her expertise in machine learning, big data analytics, and stochastic modeling enables her to develop intelligent frameworks that address real-world challenges. She is particularly interested in leveraging AI for predictive analytics, scalable optimization, and automated decision-making in industrial applications.

Awards

Dr. Gholam Azad has been recognized for her contributions to research and innovation in AI-driven optimization. Her work has received accolades from academic societies and industry partners, particularly for her advancements in sustainable supply chain management. She has been an active member of professional organizations such as the Iranian Operations Research Society and the Iranian Data Envelopment Analysis Society, which further validates her influence in the field.

Publications

“Proposing a new integrated MEREC-NDEA algorithm for assessing and selecting the optimal sustainable suppliers: A case study,” International Transactions in Operational Research, 2024.

“Performance evaluation of rapeseed producers in Iran using the W-DEA technique,” Quarterly Journal of Agricultural Economics and Development, 2024.

“Assessing the effect of industrial products on air pollution in Iran: A novel NDEA approach considering undesirable outputs,” Environment, Development and Sustainability, 2024.

“Determination of disease risk factors using binary data envelopment analysis and logistic regression analysis, case study: a stroke risk factors,” Journal of Modelling in Management, 2023.

“Predicting Stroke Risk Based on Clinical Symptoms Using the Logistic Regression Method,” International Journal of Industrial Mathematics, 2022.

“Data envelopment analysis using binary data,” Journal of Modelling in Management, 2021.

“Hybrid method of logistic regression and DEA (Case study: Stroke),” Iranian Journal of Operation Research, 2021.

Conclusion

Dr. Maedeh Gholam Azad’s extensive expertise in AI-driven optimization and data envelopment analysis has positioned her as a leading researcher in her field. Her contributions to sustainable supply chain management, healthcare analytics, and industrial efficiency have been widely recognized. Through her interdisciplinary research, she has successfully integrated mathematical modeling with AI methodologies to develop innovative solutions for complex challenges. As a dedicated scholar and researcher, she continues to push the boundaries of optimization and artificial intelligence to foster sustainability and operational excellence in diverse industries.