Xiaomu Song, PhD
- Coordinator of Robotics Engineering
- Associate Professor
Programs I Teach
- PhD, Electrical Engineering (2005)
Oklahoma State University (OK)
At Widener, I have taught a variety of undergraduate and graduate lecture and laboratory courses offered on campus or online. I enjoy interacting with students inside and outside of the classroom, encourage them to ask questions, have group discussions, explore different solutions, and be creative.
I have been advising undergraduate and graduate students in different research projects, and directing the Undergraduate Student Research Program at the School of Engineering. By conducting research with faculty advisors, students may have opportunities to apply the learned knowledge to practical applications, and gain additional experiences and skills that might not be sufficiently covered by the regular courses.
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.
- 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
- 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)