Xiaopeng Zhao, BSc MSc PhD
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Affiliation(s):
The University of Tennessee
ORCID URL:
Areas of Interest:
Social Robotics, Medical Informatics, Cognitive Rehab, artificial intelligence
Biography & Research:
Dr. Xiaopeng Zhao is a professor of mechanical, aerospace, and biomedical engineering at the University of Tennessee, Knoxville. He received BS and MS degrees in engineering mechanics in 1996 and 1999 respectively from Tsinghua University, China. He received Ph.D. in engineering science and mechanics in 2004 from Virginia Tech. He worked as a postdoctoral research associate in biomedical engineering at Duke University in 2005-2007. Dr. Zhao joined the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee, Knoxville in 2007 and has become a full professor since 2019.
Dr. Zhao is the project director on Detection, Care, and Treatment of Alzheimer’s Disease and Related Dementia, a research consortium that brings together researchers, scientists, students, and clinicians across the state of Tennessee and neighboring states to develop innovative cross-disciplinary techniques to improve the quality of life for people with ADRD as well as their caregivers. He serves as the faculty lead for the Brain-computer Interface (BCI) Community of Scholars at UTK and as a joint faculty professor at the Bredesen Center. His research focuses on neural engineering, BCI, robotics, and artificial intelligence. He has broad training and expertise on biomedical signal processing, machine learning, dynamics and control, electrophysiology, data mining, data analytics, and computer simulations. His lab has developed EEG biomarkers for detecting cognitive deficits such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and traumatic brain injury (TBI). His lab has also developed EEG-based BCI for controlling robotic devices, including social robots, drones, remote-controlled cars, and robotic arms. His lab has developed award-winning computer algorithms suitable for improving qualities of physiological signals.
