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Xiaomu Song PhD

Associate Professor

  • PhD Electrical Engineering 2005

    Oklahoma State University (OK)

  • EE 659 Digital Signal Processing

  • ENGR 219 Electric Circuits

  • ENGR 315 Probability, Statistics, & Random Processes

My research interests include pattern recognition and machine learning, from fundamental modeling issues to multidisciplinary applications in biomedical imaging, signal/image processing, computer vision, bioinformatics, and remote sensing.

Selected 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

Selected 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

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