Florian Tieves | Production Engineering | Editorial Board Member

Published on by Petroleum Engineering

Dr. Florian Tieves | Production Engineering | Editorial Board Member

Senior Researcher at Institut of Biochemistry II |  HHU Düsseldorf | Germany

Dr. Florian Tieves is a distinguished biochemist recognized for his contributions at the intersection of academic research and industrial biotechnology, with a strong focus on biocatalysis, enzyme engineering, and innovative biochemical process development. His work spans the advancement of P450-catalyzed reactions, unspecific peroxygenases, and whole-cell biocatalytic systems, where he has consistently driven progress in enzymatic oxyfunctionalization and pathway optimization. Renowned for integrating scientific insight with practical application, he has played a pivotal role in pioneering fermentation strategies, refining protein and peptide production workflows, and elevating downstream processing techniques to meet complex industrial demands. Throughout his career, he has demonstrated an exceptional ability to lead multidisciplinary research initiatives, cultivate high-impact collaborations, and translate biochemical innovation into scalable, high-performance solutions. His expertise extends across analytical development, process optimization, and the engineering of robust enzymatic systems, positioning him as a catalyst for technological advancement in modern biotechnology. Dr. Tieves is widely regarded for his strategic vision, rigorous scientific approach, and commitment to advancing enzyme-driven transformations that support sustainable, efficient, and future-ready biochemical manufacturing.

Profile: Scopus

Featured Publications

Tieves, F., et al. (2025). Engineering the Tobacco Etch Virus protease toward a platform for traceless cleavage using distal site prediction and smart library design.

Tieves, F., et al. (2025). Identification of key active-site positions controlling the chemoselectivity of Aspergillus brasiliensis unspecific peroxygenase.

Tieves, F., et al. (2025). Enzymatic valorization of fatty acids in oleochemical synthesis.

Sina Sayardoost Tabrizi | Data Analytics in Upstream Operations | Best Researcher Award

Published on by Petroleum Engineering

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.