Ziyang Zhang

As a bioengineering researcher with advanced degrees in Bioengineering and Instrument Science and Technology, I have dedicated my career to developing innovative measurement and training technologies to address critical healthcare challenges and enhance human well-being and human performance. My research spans three key areas: non-invasive medical diagnostics, medical skill assessment, and human digital twin development.

I have extensive experience in precise, non-invasive blood component measurement using near-infrared spectroscopy, with a strong focus on optimizing signal acquisition, processing, and analysis. Building on my hardware and software engineering expertise, I developed sensor-based medical skill training simulation prototypes that provide objective performance assessments. This work, supported by NIH funding and recognized with a patent, exemplifies my ability to translate research into impactful innovations. My proficiency in signal processing from diverse sensor systems, combined with advanced statistical, machine learning, and deep learning techniques, enables me to investigate and identify key factors influencing medical skill assessment and human performance under varying interventions. In addition, I am proficient in programming languages such as C++/C, Python, R, and MATLAB, allowing me to create robust computational solutions for complex biomedical and engineering challenges.

Through an interdisciplinary approach that integrates electrical engineering, bioengineering, and data science, I can contribute to the broader goals of advancing innovation in various fields.