Published on 07/10/2025 by Petroleum Engineering 57 / 100 Powered by Rank Math SEO SEO Score Dr. Sina Sayardoost Tabrizi | Data Analytics in Upstream Operations | Best Researcher Award Senior Lecturer and Director of Dean’s Office at University of Tehran | Iran Dr. Sina Sayardoost Tabrizi is a Senior Lecturer and Director of the Dean’s Office at the University of Tehran (Kish International Campus), specializing in industrial management, artificial intelligence, and sustainable operations. He holds a Ph.D. in Industrial Management and both B.Sc. and M.Sc. degrees in Computer Engineering, establishing a strong interdisciplinary foundation bridging technology and management. With over eight years of academic experience, he has supervised more than 30 student projects and published over 15 peer-reviewed papers in Q1 and Scopus-indexed journals. Dr. Sina Sayardoost Tabrizi is the founder of the Metaverse and Emerging Technologies Laboratory, where he leads innovative projects integrating AI, data analytics, and digital transformation across education, industry, and sustainability sectors. His research spans sustainable supply chain management, dynamic network data envelopment analysis (DNDEA), machine learning, and decision-support systems, with notable contributions to the petrochemical and manufacturing industries. His scholarly output includes 8 major research projects, a co-authored academic textbook, and one patent under process for an AI-driven decision-support framework. Beyond academia, he has collaborated with national and international partners—including Muscat University—on consultancy and innovation initiatives focusing on efficiency optimization, digital learning platforms, and AI ethics. As a reviewer for international journals in operations research and a member of professional organizations such as IFORS and the Iranian Association of Management Science, he contributes actively to the global research community. His work has received recognition for integrating advanced computational models with sustainable industry practices, improving efficiency in resource-intensive systems by measurable margins. Dr. Sina Sayardoost Tabrizi commitment to interdisciplinary research, educational innovation, and technological ethics positions him as a forward-thinking academic leader dedicated to advancing responsible AI, sustainability, and data-driven decision-making for societal and industrial progress. Profile: Google Scholar Featured Publications Sayardoost Tabrizi, K. S., Sabzian, A., & Moeini, A. (2024). TAM-based model for evaluating learner satisfaction of e-learning services: Case study—E-learning system of University of Tehran. Sayardoost Tabrizi, S., Yakideh, K., Moradi, M., & Ebrahimpour, M. (2025). Clustering with machine learning and using NDEA in development planning: A case study in the petrochemical two-stage SSC. Sayardoost Tabrizi, S., Yakideh, K., Moradi, M., & Ebrahimpour, M. (2024). Assessing sustainability of supply chain performance using machine learning and network data envelopment analysis. Sayardoost Tabrizi, S., Sabzian, A., & Moeini, A. (2024). A hybrid model for evaluation of e-learning user satisfaction with TAM and ELQ approach. Sayardoost Tabrizi, S., Abideen, A. Z., & Moeini, A. (2025). A novel machine learning and DNDEA framework for sustainable efficiency measurement in a circular two-stage supply chain. Sayardoost Tabrizi, S., Yousefi, S., & Yakideh, K. (2025). Forecasting efficiency of two-stage petrochemical sustainable supply chains using deep learning and DNDEA model. Operations Research Perspectives. Sina Sayardoost Tabrizi | Data Analytics in Upstream Operations | Best Researcher Award
Dr. Sina Sayardoost Tabrizi is a Senior Lecturer and Director of the Dean’s Office at the University of Tehran (Kish International Campus), specializing in industrial management, artificial intelligence, and sustainable operations. He holds a Ph.D. in Industrial Management and both B.Sc. and M.Sc. degrees in Computer Engineering, establishing a strong interdisciplinary foundation bridging technology and management. With over eight years of academic experience, he has supervised more than 30 student projects and published over 15 peer-reviewed papers in Q1 and Scopus-indexed journals. Dr. Sina Sayardoost Tabrizi is the founder of the Metaverse and Emerging Technologies Laboratory, where he leads innovative projects integrating AI, data analytics, and digital transformation across education, industry, and sustainability sectors. His research spans sustainable supply chain management, dynamic network data envelopment analysis (DNDEA), machine learning, and decision-support systems, with notable contributions to the petrochemical and manufacturing industries. His scholarly output includes 8 major research projects, a co-authored academic textbook, and one patent under process for an AI-driven decision-support framework. Beyond academia, he has collaborated with national and international partners—including Muscat University—on consultancy and innovation initiatives focusing on efficiency optimization, digital learning platforms, and AI ethics. As a reviewer for international journals in operations research and a member of professional organizations such as IFORS and the Iranian Association of Management Science, he contributes actively to the global research community. His work has received recognition for integrating advanced computational models with sustainable industry practices, improving efficiency in resource-intensive systems by measurable margins. Dr. Sina Sayardoost Tabrizi commitment to interdisciplinary research, educational innovation, and technological ethics positions him as a forward-thinking academic leader dedicated to advancing responsible AI, sustainability, and data-driven decision-making for societal and industrial progress. Profile: Google Scholar Featured Publications Sayardoost Tabrizi, K. S., Sabzian, A., & Moeini, A. (2024). TAM-based model for evaluating learner satisfaction of e-learning services: Case study—E-learning system of University of Tehran. Sayardoost Tabrizi, S., Yakideh, K., Moradi, M., & Ebrahimpour, M. (2025). Clustering with machine learning and using NDEA in development planning: A case study in the petrochemical two-stage SSC. Sayardoost Tabrizi, S., Yakideh, K., Moradi, M., & Ebrahimpour, M. (2024). Assessing sustainability of supply chain performance using machine learning and network data envelopment analysis. Sayardoost Tabrizi, S., Sabzian, A., & Moeini, A. (2024). A hybrid model for evaluation of e-learning user satisfaction with TAM and ELQ approach. Sayardoost Tabrizi, S., Abideen, A. Z., & Moeini, A. (2025). A novel machine learning and DNDEA framework for sustainable efficiency measurement in a circular two-stage supply chain. Sayardoost Tabrizi, S., Yousefi, S., & Yakideh, K. (2025). Forecasting efficiency of two-stage petrochemical sustainable supply chains using deep learning and DNDEA model. Operations Research Perspectives.