Wei Wu

I am a visiting student in the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology from Tsinghua University. My current research primarily focuses on devising signal processing tools for neural system analysis. In the Neuroscience Statistics Research Laboratory, I have two ongoing collaborative projects with researchers at both MIT and MGH:
1) Probabilistic framework for learning robust spatial/temporal/spectral patterns from EEG/MEG/fMRI data;  
2) Developing methods to quantify signal-to-noise ratios for neural systems from multi-unit spike train data.
Contact Information:
Office Address: Massachusetts Institute of Technology, 77 Massachusetts Avenue, 46-6057, Cambridge, 02139, MA 
Phone: 617-324-1881
E-mail: weiwu at neurostat dot mit dot edu
Research Interests:
Statistical Modeling for Multichannel EEG/MEG

Statistical Modeling for fMRI Data
Statistical Modeling for Neural Encoding & Decoding
Information Geometry
Selected Publications:

Wei Wu, Zhe Chen, Shangkai Gao, and Emery N. Brown. A Hierarchical Bayesian Approach for Learning Sparse Spatio-Temporal Decompositions of Multichannel EEG, Neuroimage, in press.
Wei Wu, Zhe Chen, Shangkai Gao, and Emery N. Brown. Hierarchical Bayesian Modeling of Inter-Trial Variability and Variational Bayesian Learning of Common Spatial Patterns from Multichannel EEG, IEEE ICASSP'2010.

Wei Wu, Zhe Chen, Shangkai Gao, and Emery N. Brown. A Probabilistic Framework for Learning Robust Common Spatial Patterns, IEEE EMBC'2009.
Wei Wu, Xiaorong Gao, Bo Hong, and Shangkai Gao. Classifying Single-Trial EEG during Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL), IEEE Transactions on Biomedical Engineering, vol. 55, pp. 1733-1743, 2008.
Zhonglin Lin, Changshui Zhang, Wei Wu, and Xiaorong Gao. Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-based BCIs, IEEE Transactions on Biomedical Engineering, vol. 53, pp. 2610-2614, 2006.
Wei Wu, Xiaorong Gao, and Shangkai Gao. One-Versus-the-Rest (OVR) Algorithm: an Extension of Common Spatial Patterns (CSP) algorithms to Multi-class Case, Proceedings of 27th IEEE EMBC, 2005.
Best Presentation Award, The 164th Academic Forum for Ph.D. Students, Tsinghua University, 2007.
Yi-Chi Mei Memorial Scholarship, Tsinghua University, 2007.
First Prize in the Third National Graduate Mathematical Contest in Modeling, 2006.
IBRO-APRC Awards in RIKEN Brain Science Institute, 2006.
Professional Activities:
IEEE Student Member
Regular reviewer of Neuroimage, Clinical Neurophysiology, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE Transactions on Signal Processing, IEEE Signal Processing Letters, Pattern Recognition, Brain Topography, Computational Intelligence and Neuroscience, Cognitive Neurodynamics, et al.

Program committee, Satellite Symposium of IEEE EMBS 27th annual conference -  Frontiers of Neural Engineering. 2005

Fall 2009

6.079 : Introduction to Convex Optimization (T&R 2.30-4:00  (4-370), Instructor: Stephen Boyd, Pablo A Parrilo)
6.804 : Computational Cognitive Science (T&R3.30-5  (46-3189), Instructor: Joshua B. Tenenbaum)
6.438 : Algorithms for Inference (T&R9.30-11  (32-124), Instructor: G. W. Wornell, W. T. Freeman)
Spring 2009
9.073 : Statistics for Neuroscience Research (Mon&Wed 9:00-10:30 am, 46-3310. Instructor: Emery N. Brown)
9.301 : Neural Plasticity: Learning and Memory (Instructor: Mark Bear, Susumu Tonegawa, Matthew A. Wilson)
9.520 : Statistical Learning Theory and Applications (Mon&Wed 10:30-12:00 am, 46-5193. Instructor: Tomaso Poggio, Ryan Rifkin, Jake Bouvrie, Lorenzo Rosasco)
9.641 : Introduction to Neural Networks (Mon&Wed 3:00-4:30 pm, 46-5056. Instructor: H. Sebastian Seung)
6.252 : Nonlinear Programming (Tue&Thr 1:00-2:30 pm, 32-155. Instructor: Dimitri P. Bertsekas)
6.437 : Inference and Information (Tue&Thr 9:30-11:00 am, 2-105. Instructor: Polina Golland, Gregory W. Wornell)
Fall 2008 
9.01 : Introduction to Neuroscience (Instructor: Mark Bear, H. Sebastian Seung)
9.07 : Statistics for Brain and Cognitive Sciences (Instructor: Emery N. Brown)
9.011 : Systems Neuroscience (Instructor: Earl K. Miller, Matthew A. Wilson)
6.341 : Discrete-Time Signal Processing (Instructor: A. V. Oppenheim, V. K. Goyal)
6.867 : Machine Learning (Instructor: Tommi Jaakkola, Michael Collins)
6.436 : Fundamentals of Probability (Instructor: John N. Tsitsiklis)
HST. 583 : fMRI: Data Acquisition and Analysis (Instructor: Randy Gollub, et al.)