Office: Neill 409
Phone: (509) 335-3161
Fax: (509) 335-1188
- 2021, Associate Professor, Department of Mathematics and Statistics, Washington State University
- 2015 - 2021, Assistant Professor, Department of Mathematics and Statistics, Washington State University
- 2013 - 2015, Postdoctoral Fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center
- 2008 - 2013 Ph.D. in Statistics, Colorado State University
- 2004 - 2008 B.S. in Statistics, The University of Science and Technology of China
- Statistical learning of complex data objects including trees, curves and images
- Bayesian variable selection
- compressive sensing and system design
Y. Wang, J. S. Marron, L. A. Aydin, E. Bullitt and H. Wang (2012). A Nonparametric Regression Model with Tree-structured Response, Journal of the American Statistical Association, 107: 1272-1285.
Y. Wang, H. Wang, and L. L. Scharf (2014). Optimum Compression of a Noisy Measurement for Transmission over a Noisy Channel, IEEE Transactions on Signal Processing, 62: 1279-1289.
Y. Wang, H. Wang, and L. L. Scharf (2014). The Geometry of Fusion Inspired Channel Design, Signal Processing, 99: 136-146.
Y. Wang, B. P. Hobbs, J. Hu, C. S. Ng, and K. A. Do (2015). Predictive Classification of Correlated Targets with Application to Detection of Metastatic Cancer using Functional CT Imaging, Biometrics, 71(3):792-802.
Y. Wang, B. P. Hobbs, and C. S. Ng (2015). CT Perfusion Characteristics Identify Metastatic Sites in Liver, BioMed Research International, 120749 (invited).
Z. Zhang, Y. Wang, E. K. P. Chong, and A. Pezeshki (2017). Subspace Selection for Projection Maximization with Matroid Constraints, IEEE Transactions on Signal Processing, 65: 1339-1351.
I. Santamaria, L. Scharf, J. Via, H. Wang, Y. Wang (2017). Passive Detection of Correlated Subspace Signals in Two MIMO Channels, IEEE Transactions on Signal Processing, 65: 5266-528.
Y. Wang, J. Hu, K. Do, and B. P. Hobbs (2019). An Efficient Nonparametric Estimate for Spatially Correlated Functional Data, Statistics in Bioscience, 11:162-183.
L. Paxton, Y. Caog, K. Vixie, Y.Wang, C. S. Ng, B. P. Hobbs (2019), A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors, Journal of Abstract and Computational Mathematics, 4: 081-093.
Y. Wang, J. Hu, C. S. Ng, and B. P. Hobbs (2020). A Functional Model for Classifying Metastatic Lesions Integrating Scans and Biomarkers, Statistical Methods in Medical Research, 29(1):137-150.
- STAT 360 Probability and Statistics (Fall 15, 16, 17, 18)
- STAT 370 Introductory Statistics for Engineers (Spring 16, Fall 19)
- STAT 423/523 Statistical Methods for Engineers and Scientists (Spring 17, 19, 20)
- MATH 583 Topic Class in Bayesian Analysis (Fall 16)
- STAT 574 Linear and Nonlinear Mixed Model (Fall 17)
- STAT 576 Bayesian Analysis (Fall 18)
- STAT 575 Theory of Multivariate Analysis (Fall 19)
- Yufeng Cao. Experiments in Medical Image Segmentation, Spring 2019, jointed with K. Vixie.
- Yuanhong Song. Modeling Spatial-Temporal Variability of LANDSAT-MODIS Fused NDVI for Crops in the Palouse Region, Summer 2019, joint with Z. Zhang.
- Hang Guo. Deep Learning for Lung Cancer Classification, Summer 2019.
- Syed Badruddoza. Classification with Sparse and Irregular Sampled Observations, Spring 2020.