Seyed Ghiaasiaan | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

Published on by Petroleum Engineering

Dr. Seyed Ghiaasiaan | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

Assistant Professor at University of Texas Permian Basin | United States

Dr. Seyed Reza Ghiaasiaan is an accomplished materials and mechanical engineer whose research bridges advanced manufacturing, metallurgy, and applied mechanics, contributing significantly to innovations in additive manufacturing and metal processing. As a faculty member at the University of Texas Permian Basin’s Advanced Manufacturing Center, he integrates multidisciplinary approaches to study process–structure–property–performance relationships across metallic alloys and composites, including nickel-based superalloys, titanium, stainless steel, and aluminum systems. His extensive academic and industrial experience spans roles in research, teaching, and engineering design in Canada, the United States, and internationally, fostering collaborations with major research institutions and industrial partners such as NASA and NSERC. Dr. Ghiaasiaan has authored more than forty-five peer-reviewed publications and book chapters, with his work frequently cited in the fields of solidification, fatigue, and mechanical performance of materials. His contributions to the understanding of additive manufacturing processes, microstructural evolution, and fatigue-critical applications in aerospace and energy sectors have advanced the frontier of materials engineering. He has been repeatedly recognized for research excellence, scholarly reviewing, and mentorship, earning awards from institutions and professional bodies alike. A licensed Professional Engineer and active member of multiple international societies including ASME, ASM, and TMS, Dr. Ghiaasiaan exemplifies the integration of research rigor, innovation, and professional service, shaping the next generation of engineers through his leadership in advanced materials manufacturing and sustainable engineering design.

Profile: Google Scholar

Featured Publications

Mostafaei, A., Zhao, C., He, Y., Ghiaasiaan, S. R., Shi, B., Shao, S., Shamsaei, N., et al. (2022). Defects and anomalies in powder bed fusion metal additive manufacturing.

Mostafaei, A., Ghiaasiaan, R., Ho, I. T., Strayer, S., Chang, K. C., Shamsaei, N., et al. (2023). Additive manufacturing of nickel-based superalloys: A state-of-the-art review on process-structure-defect-property relationship.

Azizi, H., Zurob, H., Bose, B., Ghiaasiaan, S. R., Wang, X., Coulson, S., Duz, V., et al. (2018). Additive manufacturing of a novel Ti-Al-V-Fe alloy using selective laser melting.

Azizi, H., Ghiaasiaan, R., Prager, R., Ghoncheh, M. H., Samk, K. A., Lausic, A., et al. (2019). Metallurgical and mechanical assessment of hybrid additively-manufactured maraging tool steels via selective laser melting.

Ghiaasiaan, R., Amirkhiz, B. S., & Shankar, S. (2017). Quantitative metallography of precipitating and secondary phases after strengthening treatment of net shaped casting of Al-Zn-Mg-Cu (7000) alloys.

Jialing Zou | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

Published on by Petroleum Engineering

Dr. Jialing Zou | Artificial Intelligence in Petroleum Engineering | Best Researcher Award

Lecturer at Southwest Petroleum University | China

Dr. Jialing Zou is a distinguished Lecturer at the School of Mechatronic Engineering, Southwest Petroleum University, recognized for her innovative contributions to intelligent sensing, data mining, and safety assessment within petroleum engineering. She holds a Ph.D. in Mechanical Engineering and focuses her research on developing intelligent early warning systems, smart fluid sensing, and real-time monitoring technologies to improve the safety and efficiency of complex drilling operations. Her scholarly pursuits combine data-driven intelligence with mechanical system optimization, advancing digital transformation and automation in the oil and gas industry. Dr. Zou has served as the principal investigator for several competitive research projects supported by major national and provincial funding agencies, including the National Natural Science Foundation of China, the Sichuan Provincial Science and Technology Department, and Southwest Petroleum University’s research initiatives. Her leadership in projects such as intelligent decision-making for digital geosteering and the development of online sensing methods for drilling fluid rheological parameters has significantly contributed to the field’s technological innovation. She has also played a key role in multiple national-level programs, including those under the National Key R&D Program of China, focusing on intelligent diagnosis and control technologies for petroleum exploration. Dr. Zou’s research has been widely published in high-impact journals 539 Citations by 460 documents 16 Documents h-index: 7 where her studies on advanced algorithms, fuzzy theory applications, and data-driven modeling have been highly regarded. Her interdisciplinary expertise bridges mechanical engineering, artificial intelligence, and safety science, positioning her as a leading emerging researcher in intelligent drilling and predictive safety systems. Through her academic excellence and commitment to innovation, Dr. Zou continues to advance knowledge, mentor young engineers, and promote the integration of intelligent technologies to enhance the sustainability and safety of petroleum engineering operations.

Profiles: Scopus | ORCID

Featured Publications

Yang, H., Liu, J., Yang, Z., Liang, H., Zhang, L., & Zou, J. (2023). Active control of the fluid pulse based on the FxLMS.

Liang, H., Xiong, J., Yang, Y., & Zou, J. (2023). Research on intelligent recognition technology in lithology based on multi-parameter fusion.

Liang, H., Xiong, J., Yang, Y., & Zou, J. (2023). Research on intelligent recognition technology in lithology based on multiparameter fusion of logging while drilling.

Liang, H., Zhang, W., Xu, W., Yang, H., & Zou, J. (2023). Research on rheological parameters correction method based on pipe viscometer.

Zhang, H., Chen, Q., Ni, P., Liang, H., Mao, M., & Zou, J. (2022). Study on the intelligent identification method of formation lithology by element and gamma spectrum.

Yang, H., Zhang, L., Li, L., Liang, H., & Zou, J. (2021). Error analysis and accuracy calibration method of U-tube Coriolis mass flowmeter under pulsating flow.