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Xiangru Li

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417B Thomas Building
University Park, PA 16802
Phone: (814) 863-1607


  1. Ph.D. in Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
  2. M.S. in Probability and Statistics, Jilin University
  3. B.S. in Mathematics, Shandong Normal University

Selected Publications

[1] Xiangru Li,  Ruyang Pan,  and Fuqing Duan.  Parameterizing Stellar Spectra Using Deep Neural Networks.  Research in Astronomy and Astrophysics 17(4) : 036,  2017.
[2] Wei Gao, Xiangru Li(Correspondence author). Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization. Chinese Astronomy and Astrophysics, 41(3):331-346, 2017.
[3] Xiangru Li, Yu Lu, Georges Comte, Ali Luo, Yongheng Zhao and YongjunWang. Linearly supporting feature extraction for estimating stellar atmosphericparameters.  The Astrophysical Journal Supplement Series, 218(1),3, 2015
[4] Xiangru Li, Q.M. Jonathan Wu, Ali Luo, Yongheng Zhao, Yu Lu, Fang Zuo, Tan Yang, Yongjun Wang. SDSS/SEGUE spectral feature analysis for stellar atmospheric parameter estimation. The Astrophysical Journal, 790 105, 2014.
[5]Tan Yang, Xiangru Li(Correspondence author). Autoencoder of Stellar Spectra and its Application  in  Automatically  Estimating Atmospheric Parameters. Monthly Notices of the Royal Astronomical Society, 452 (1): 158-168, 2015.
[6]Yu Lu, Xiangru Li (Correspondence author). Estimating Stellar Atmospheric Parameters Based on LASSO and  Support Vector Regression.  Monthly  Notices Royal Astronomical Society, 452(2):1394-1401, 2015.
[7] Xiangru Li, Zhanyi Hu. Rejecting Mismatches by Correspondence Function. International Journal of Computer Vision, 89(1):1-17,2010.8.
[8] Xiangru Li, Zhanyi Hu, Fuchao Wu. A note on the convergence of the mean shift. Pattern Recognition, 40(6):1756-1762,2007.6.
[9] Xiangru Li, Xiaoming Li, Hailing Li, Maoyong Cao. Rejecting Outliers based on Correspondence Manifold. Acta Automatica Sinica, 35(1):17-22, 2009.1.
[10]Xiangru Li. Feature Extracting Methods in Spectrum Data Mining. Progress in Astronomy, 30(1):94-105, 2012.

Research Interests

Data Mining, Bayesian Learning, Astronomy Data Processing, Computer Vision

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