Astrostatistics and Astroinformatics
Multiwavelength astronomy
Photometric redshift estimation of quasars and galaxies
Classification and regression in astronomy
Data analysis, reduction and mining

Astronomy in the Big Data Era, Zhang, Y., Zhao, Y., Data Science Journal, 2015, 14(11), pp.1-9Statistical Study of 2XMMi-DR3/SDSS-DR8 Cross-correlation Sample, Zhang,Yan-xia, Zhou, Xin-lin, Zhao, Yong-heng, Wu, Xue-bing, 2013, AJ, 145,42-54

Morphology Classification and Photometric Redshift Measurement of Galaxies, Zhang, Yanxia, Li, Lili, Zhao, Yongheng, 2009, MNRAS, 392,233-239Estimating Photometric Redshifts of Quasars via K-nearest Neighbors Approach Based on Large Survey Databases, Zhang, Yanxia, Ma, He, Peng, Nanbo, Zhao, Yongheng, Wu, Xue-bing, 2013, AJ, 146, 22-31

Classification in Multidimensional Parameter Space: Methods and Examples, Zhang, Yanxia,  Zhao, Yongheng. 2003, PASP, 115, 1006-1018

Automated clustering algorithms for classification of astronomical objects, Zhang, Yanxia, Zhao, Yongheng. 2004, A&A, 422, 1113-1121

Selecting Quasar Candidates by a SVM Classification System, Peng, Nanbo，Zhang, Yanxia, Zhao, Yongheng and Wu, Xue-bing,2012,MNRAS,425,2599-2609

Support vector machines and kd-tree for separating quasars from large survey data bases, Gao, Dan, Zhang, Yanxia, Zhao,Yongheng, 2008, MNRAS, 386(3), 1417-1425

Kernel Regression For Determining Photometric Redshifts From Sloan Broadband Photometry, Wang, Dan, Zhang, Yan-Xia et al., 2007, MNRAS, 382, 1601 -1606

Comparison of decision tree methods for finding active objects, Zhao, Yongheng, Zhang, Yanxia, 2008, Advances in Space Research, 41(12), 1955-1959