Education
Ph.D., Industrial (Manufacturing focus) Engineering, Texas Tech University, Lubbock, Texas (2025)
M.Sc, Mechanical Engineering, University of Louisiana at Lafayette (2018)
B.Sc, Mechanical Engineering, IUT-OIC, Dhaka, Bangladesh (2016)
Bio
Imtiaz Qavi, Ph.D., is a tenure-track assistant professor of mechanical engineering at Marian University Indianapolis, where he began his appointment in August 2025. His academic and research expertise spans data-driven manufacturing process optimization, mechanical and manufacturing system design, and the application of machine learning and deep learning for defect detection in advanced manufacturing processes.
Before joining Marian University, Dr. Qavi served as a graduate research assistant at Texas Tech University from August 2020 to August 2025, completing his Ph.D. in Manufacturing Engineering with a focus on advanced manufacturing, bioprinting, and AI-driven process modeling. He previously conducted research at the University of Louisiana at Lafayette from August 2018 to July 2020 on the spray characteristics of alternative aviation fuels and gained additional research experience at Baylor University in late 2019. His industry background includes an engineering role at Energypac Engineering (February 2017–August 2018), where he worked on the design and manufacturing of switchgear and busbar systems for national power-grid distribution systems in Bangladesh.
Courses Taught
Research Interests
Dr. Qavi’s research expertise spans both micro- and macro-scale additive manufacturing, with a strong emphasis on biomedical applications. His work integrates data-driven mathematical optimization, enabling the design of robust, constraint-aware, and high-performance manufacturing systems. He has published multiple Q1 journal articles in AI-driven manufacturing, optimization, and advanced process modeling.
His research portfolio also extends into combustion and droplet fluid dynamics, including atomization, combustion behavior, and micro-droplet–based fabrication methods relevant to electrohydrodynamic jetting. This combination of advanced manufacturing and multiphase flow research enables cross-disciplinary approaches to process reliability and material innovation.
