معرفی
Dr. Mingzi Zhang is a Research Fellow in the Faculty of Engineering at the University of New South Wales, working with Dr. Susann Beier. He maintains dual affiliations as an Honorary Research Fellow at Macquarie Medical School and Collaborative Researcher at Tohoku University's Biofluid Dynamics Lab in Japan.
His educational background includes:
- PhD in Engineering from Tohoku University (Japan), focusing on structural optimisation of neurovascular stents
- PhD in Biomedical Sciences from Macquarie University, specializing in virtual stenting for cerebral aneurysms
- Master's research developing lumped parameter models of the human circulatory system
Dr. Zhang specializes in computational modeling of cardiovascular diseases, with expertise in stent optimization, surgical implant deployment, and non-invasive hemodynamic parameter estimation. His research bridges engineering principles with clinical applications, particularly through his virtual stenting platform that enables neuro-interventionists to simulate and optimize treatment strategies for intracranial aneurysms before actual procedures.
His scholarly contributions include over 25 peer-reviewed journal articles, 2 book chapters, and presentations at more than 20 academic conferences. His work demonstrates strong translational potential, with core technologies being commercialized in Japan alongside 3D printing methods for vascular models.
Professional recognition includes:
- Membership in The Japanese Society of Mechanical Engineers (JSME)
- Membership in the Institute of Electrical and Electronics Engineers (IEEE)
- Membership in the Institution of Engineers Australia (EA)
- Peer reviewer for leading journals including Computers in Biology and Medicine and Journal of Vascular and Interventional Radiology
Dr. Zhang serves as Guest Lecturer for Applied Bio and Microfluidics (MECH9650) at UNSW and leads Collaborative Research Projects (J21I074 & J22I075) at Tohoku University (2021-2025) as Principal Investigator. His research program demonstrates consistent commitment to translating computational models into clinical tools that improve patient care.
