Wei Wu received the PhD degree in Biomedical Engineering from Tsinghua University, China, in 2012. From 2008 to 2010, he was a visiting student at the Neuroscience Statistics Laboratory, MIT, directed by Dr. Emery Brown. He is an associate editor of Neurocomputing and a member of IEEE Biomedical Signal Processing Technical Committee.

Professional Education

  • Doctor of Philosophy, Tsinghua University (2012)

Stanford Advisors


All Publications

  • Bayesian Machine Learning IEEE SIGNAL PROCESSING MAGAZINE Wu, W., Nagarajan, S., Chen, Z. 2016; 33 (1): 14-36
  • STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging INTERNATIONAL JOURNAL OF NEURAL SYSTEMS Liu, K., Yu, Z. L., Wu, W., Gu, Z., Li, Y. 2015; 25 (4)


    For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.

    View details for DOI 10.1142/S0129065715500161

    View details for Web of Science ID 000355328600005

    View details for PubMedID 25903226

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