Guangzhi Cao received his B.S. and M.S. degrees in electrical engineering from Zhejiang University, China, in 2002 and 2004, respectively. He received his Ph.D. degree in electrical engineering from Purdue University in 2009. Currently, Guangzhi is working as a scientist at GE Healthcare Technologies. His current research interests include statistical signal and image processing, inverse problems (especially related with medical and electronic imaging applications), machine learning and computer vision.

Selected Publictions:

1. G. Cao, C. Bouman, and K. Webb, "Non-Iterative MAP Reconstruction Using Sparse Matrix Representations," IEEE Trans. on Image Processing, vol. 18, issue 9, 2009.

2. G. Cao and C. Bouman, "Covariance Estimation for High Dimensional Data Vectors Us- ing the Sparse Matrix Transform," in Advances in Neural Information Processing Systems (NIPS), MIT Press, 2008.

3. G. Cao, C. Bouman, and K. J.Webb, "Results in non-iterative MAP reconstruction for opti- cal tomography," in Proceedings of the SPIE/IS&T Conference on Computational Imaging VI, 2008.

4. G. Cao, V. Gaind, C. Bouman, and K. Webb, "Localization of an Absorbing Inhomogeneity in Scattering Medium in a Statistical Framework," Optics Letters, Vol. 32, No. 20, 2007.

5. G. Cao, C. Bouman, and K. Webb, "Fast Reconstruction Algorithms for Optical Tomog- raphy Using Sparse Matrix Representations," in Proceedings of 2007 IEEE International Symposium on Biomedical Imaging (ISBI), 2007. (Invited paper)

6. G. Cao, C. Bouman, and K.Webb, "Fast and Efficient Stored Matrix Techniques for Optical Tomography," in Proceedings of the 40th Asilomar Conference on Signals, Systems and Computers, 2006 (Invited paper).