Dr. Mohseni received his B.Sc. in 2006 in materials science, metallurgical engineering and continued his studies on to an M.Sc. program in the field of materials science, metal forming processes at the University of Tehran, Iran. Following his short experience of working in Oil & Gas industry, he showed a growing interest in the field of Non-Destructive Evaluation (NDE) and he decided to enroll in a Ph.D. program at École de Technologie Supérieure (ETS), Montreal, Canada with a focus on automated defect detection and characterization using electromagnetic NDE method and eddy current surface probes. In the course of his research activities in Canada, Dr. Mohseni had collaborations with renowned aerospace industries and manufacturers such as Pratt and Whitney Canada, Safran, L-3 MAS, Bell Helicopter, and Heroux Devtek.

Dr. Mohseni has more than 30 conference papers and journal publications mostly communicated through various prestigious international conferences and distinguished peer-reviewed NDE journals. He has been able to deploy his research knowledge and experience to co-develop agile and flexible automated robotic NDE solutions for welding, joining, and additive manufacturing technologies through his appointment as a Lecturer in the advanced and dynamic Centre of Ultrasound Engineering (CUE) research group at the Department of Electronics and Electrical Engineering of the University of Strathclyde. Dr. Mohseni also supports the Royal Academy of Engineering and Spirit AeroSystems research chair, Professor Gareth Pierce, assisting him in his endeavors of developing a state-of-art multimillion advanced Robotically Enabled Sensing hub.

Dr. Mohseni’s current research is aligned with the NDE 4.0 vision and objectives to identify and tackle future NDE technology barriers to be used in Industry 4.0. His research interest extends over different testing techniques based on electromagnetic and acoustic wave propagation principles and various applications such as Joining, additive, metal processing, and composite inspections. More specifically, multi-physics FEM for electromagnetic induction, ultrasound wave propagation and heat transfer,  multi-sensor data fusion, machine learning for intelligent NDE data interpretation, and the probability of detection studies forms most of his research undertakings.