Assist. Prof. Dr. Somayeh Najafi-Ghobadi | Econometrics | Best Researcher Award

Assist. Prof. Dr. Somayeh Najafi-Ghobadi | Econometrics | Assistant profesor at Kermanshah university of technology | Iran

Assist. Prof. Dr. Somayeh Najafi-Ghobadi is a dedicated academic and researcher whose scholarly work integrates industrial engineering, operations research, data mining, and healthcare analytics, making her a highly recognized contributor in multidisciplinary research environments. She obtained her Ph.D. in Industrial Engineering and currently serves as an Assistant Professor in the Department of Industrial Engineering at Kermanshah University of Technology, where she actively mentors students, leads research initiatives, and contributes to institutional academic excellence. Her professional experience includes extensive involvement in research projects focusing on optimization, supply chain management, and data-driven modeling, as well as collaborations with prominent scholars from institutions such as the University of Tehran, Alzahra University, and Tecnológico de Monterrey, which highlight her ability to work in international and cross-disciplinary teams. Assist. Prof. Dr. Somayeh Najafi-Ghobadi has cultivated expertise in game theory, machine learning applications, and predictive modeling, contributing to both theoretical advancements and real-world problem-solving, particularly in industrial decision-making and healthcare applications. Her research interests cover a wide range of interconnected domains, including closed-loop supply chains, new product diffusion modeling, dynamic pricing, healthcare informatics, and predictive analytics for patient outcomes. With a citation count of over 136, an h-index of 7, and an i10-index of 6, her publications in high-quality journals indexed in Scopus and IEEE demonstrate not only the visibility but also the impact of her research contributions. She has published influential works on subjects such as pricing and supplier selection in closed-loop supply chains, machine learning classifiers for predicting low birth weight, and optimization of marketing-mix strategies for new generation products, which reflect her versatility and problem-oriented approach to complex challenges. In terms of research skills, Assist. Prof. Dr. Somayeh Najafi-Ghobadi is proficient in advanced mathematical modeling, algorithm development, data mining, and statistical analysis, and she applies these skills to both industrial optimization problems and health-related predictive studies.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

Najafi-Ghobadi, S., & Esmaeili, M. (2018). A game theory model for pricing and supplier selection in a closed-loop supply chain. International Journal of Procurement Management, 11(4), 472–494. Citations: 25

Arayeshgari, M., Najafi-Ghobadi, S., Tarhsaz, H., Parami, S., & Tapak, L. (2023). Machine learning-based classifiers for the prediction of low birth weight. Healthcare Informatics Research, 29(1), 54–63. Citations: 19

Najafi-Ghobadi, S., Bagherinejad, J., & Taleizadeh, A. A. (2021). A two-generation new product model by considering forward-looking customers: Dynamic pricing and advertising optimization. Journal of Retailing and Consumer Services, 63, 102387. Citations: 19

Najafi-Ghobadi, S., Bagherinejad, J., & Taleizadeh, A. A. (2021). Modeling the diffusion of generation products in the presence of heterogeneous strategic customers for determining optimal marketing-mix strategies. Computers & Industrial Engineering, 160, 107606. Citations: 16