University of California, San Diego

Graduate Student, Electrical and Computer Engineering

About

I am a 5-th graduate student in the Department of Electrical and Computer Engineering, University of California, San Diego (UCSD). I work with Prof. Bhaskar D. Rao, Prof. Scott Makeig, and Prof. Tzyy-Ping Jung.

My research fields include:
Sparse Signal Recovery/Compressed Sensing,
Bayesian Statistics
EEG/MEG Source Localization,
fMRI Data Analysis,
Independent Component Analysis,
Computational and Cognitive Neuroscience.

Currently I am working on sparse signal recovery/compressed sensing with application to neuroimaging. Motivated by practical problems from various applications, I focus on deriving effective algorithms for practical use, namely, deriving algorithms which have satisfying recovery quality for non-sparse signals, work well in strongly noisy environment, and work well when the sensing matrix is highly coherent. To achieve this, I largely extend the basic sparse Bayesian learning (SBL) framework to spatiotemporal SBL models. With these models, I have derived:

1. T-MSBL/T-SBL and Robust-TMSBL algorithms for temporally structured signals;
2. the Block SBL framework and associated various algorithms for spatially structured signals and non-sparse signals;
3. algorithms for time-varying structured signals;
4. algorithms for signals with dynamical spatio-temporal structure and other complex structure.

All of them have better performance than most, if not all, existing algorithms in corresponding models, and show great promise in various applications, including:

EEG/MEG source localization,
telemonitoring via wireless body-area networks with ultra-low power consumption,
multi-task learning for feature extraction in neuroimaging,
brain-computer interface (BCI),
face recognition,
power spectrum estimation, and
earthquake detection.

Before 2009 I worked on independent component analysis/blind source separation with application to biomedical engineering.

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