Xiaomu Song

Xiaomu Song, PhD

  • Coordinator of Robotics Engineering
  • Associate Professor
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Programs I Teach

Education

  • PhD, Electrical Engineering (2005)
    Oklahoma State University (OK)

About Me

Prior to joining Widener, I received postdoctoral training and then worked as a research scientist at the Northshore University Healthsystem Research Institute, Evanston, Illinois, which is affiliated with the University of Chicago Pritzker School of Medicine.

I received BS and MS degrees in electrical engineering from Northwestern Polytechnical University, China, and a PhD in electrical engineering from Oklahoma State University. I was a staff system engineer at the Institute of Remote Sensing Equipment in Beijing, China, and a staff research engineer at Motorola Global Software Group in Beijing, China.

Research Interests

Dr. Song’s research interests include pattern recognition and machine learning from fundamental modeling issues to multidisciplinary applications in robotic vision, artificial intelligence, brain-machine interface, biomedical imaging, multi-sensor fusion, and signal/image processing.

Publications

  • Song, Xiaomu and Suk-Chung Yoon. "Improving Brain-Computer Interface Classification Using Adaptive Common Spatial Patterns." Computers in Biology and Medicine 61 (2015): 150–160.
  • Song, Xiaomu, Lawrence Panych, and Nan-kuei Chen. "Data Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility." Brain Connectivity 6 (2016): 136–151.
  • Song, Xiaomu, Lawrence Panych, and Nan-kuei Chen. "Spatially Regularized Machine Learning for Task and Resting-State fMRI." Journal of Neuroscience Methods 257 (2016): 214–228.
    Google Scholar Citations

Professional Affiliations & Memberships

Senior Member, Institute of Electrical and Electronic Engineers (IEEE), Phi Kappa Phi, Sigma Xi

Awards

  • Provost's Grant, Widener University (2011) (2012) (2013) (2015)
  • Faculty Development Options Grant, Widener University (2011) (2012) (2013) (2014) (2015) (2016)
  • Major Research Instrumentation Grant, National Science Foundation (2016)

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