Su-Yun Huang

 Research Fellow
Institute of Statistical Science, Academia Sinica
Taipei 11529, Taiwan
Tel: 886-2-27871965
Fax: 886-2-27831523



Ph.D. in Statistics (1990), Purdue University , USA

   Advisor: William J. Studden.

   Thesis: Density Estimation by Spline Projection Kernels.

M.S. in Mathematics(1985), National Taiwan University, Taiwan

B.S. in Mathematics(1983), National Taiwan University, Taiwan





2004-present Research Fellow, Institute of Statistical Science, Academia Sinica.

1997-2004 Associate Research Fellow, Institute of Statistical Science, Academia Sinica.

1993-1997 Assistant Research Fellow, Institute of Statistical Science, Academia Sinica.

1990-1993 Assistant Professor, Department of Mathematics, Wayne State University, U.S.A.




Research Interests


Dimension reduction, high-dimensional data analysis, machine learning & deep learning, robust statistical inference and algorithms.




Full Publication List 


1. Huang, S. and Huang, S.Y. (2021). On the asymptotic normality and efficiency of Kronecker envelope principal component analysis. J. Multivariate Analysis, accepted.

2. Chen, T.L., Huang, S.Y. and Wang, W. (2020). A consistency theorem for randomized singular value decomposition. Statistics and Probability Letters, 161, 108743.

3. Cheng, Y.H., Huang, T.M. and Huang, S.Y. (2020). Tensor decomposition for dimension reduction. WIREs Computational Statistics, 12:e1482.

4. Chung, S.C., Wang, S.H., Niu, P.Y., Huang, S.Y., Chang, W.H. and Tu, I.P. (2020). Twostage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy. Annals of Mathematical Sciences and Applications, 5, 283-316.

5. Wang, S.H., Huang, S.Y. and Chen, T.L. (2020). On asymptotic normality of cross data matrix-based PCA in high dimension low sample size. J. Multivariate Analysis, 175, 104556.

6. Hung, H. and Huang, S.Y. (2019). Sufficient dimension reduction via random partitions for large-p-small-n problems. Biometrics, 75(1), 245-255.

7. Tu, I.P., Huang, S.Y. and Hsieh, D.N. (2019). The generalized degrees of freedom of multilinear principal component analysis. J. Multivariate Analysis, 173, 26-37.

8. Hung, H., Jou, Z.Y. and Huang, S.Y. (2018). Robust mislabel logistic regression without modeling mislabel probabilities. Biometrics, 74, 145-154.

9. Chen, T.L., Fujisawa, H., Huang, S.Y. and Huang, C.R. (2016). On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation. J. Multivariate Analysis, 143, 165-184.

10. Hung, H., Lin, Y.T., Chen, P.W., Wang, C.C., Huang, S.Y. and Tzeng, J.Y. (2016). Detection of gene-gene interactions using multistage sparse and low-rank regression. Biometrics, 72, 85-94.

11. Chen, T.L., Hsieh, D.N., Hung, H., Tu, I.P.,Wu, P.S., Wu, Y.M., Chang, W.H. and Huang, S.Y. (2014). gamma-SUP: a self-updating clustering algorithm based on minimum gammadivergence with application to cluster cryo-EM images of asymmetric particles. Annals of Applied Statistics, 8, 259-285.

12. Chen, T.L., Huang, S.Y., Hung, H. and Tu, I.P. (2014). An introduction to multilinear principal component analysis. J. Chinese Statistical Association, special issue on Machine Learning, 52, 24-43.

13. Yeh, Y.R., Huang, S.Y., Pao, H.K. and Lee, Y.J. (2014). A review of reduced kernel trick in machine learning. J. Chinese Statistical Association, special issue on Machine Learning, 52, 85-114.

14. Chen, P., Hung, H., Komori, O., Huang, S.Y. and Eguchi, S. (2013). Robust independent component analysis via minimum gamma-divergence estimation. IEEE Journal of Selected Topics in Signal Processing, 7, 614-624.

15. Chang, L.B., Bai, Z.D., Huang, S.Y. and Hwang, C.R. (2013). Asymptotic error bounds for kernel-based Nystrőm low-rank approximation matrix. Journal of Multivariate Analysis, 120, 102-119.

16. Hung, H., Wu, P.S., Tu, I.P. and Huang, S.Y. (2012). On multilinear principal component analysis of order-two tensors. Biometrika, 99, 569-583.

17. Lee, M.H., Tzeng, J.Y., Huang, S.Y. and Hsiao, C.K. (2011). Combining an evolution-guided clustering algorithm and haplotype-based LRT in family association studies. BMC Genetics, 12:48.

18. Chen, P.C., Lee, K.Y., Lee, T.J., Lee, Y.J. and Huang, S.Y. (2010). Multiclass support vector classifcation via coding and regression. Neurocomputing, 73, 1501-1512.

19. Huang, S.Y., Lee, M.H. and Hsiao, C.K. (2009). Nonlinear measures of association with kernel canonical correlation analysis and applications. J. Statist. Planning Inference, 139, 2162-2174.

20. Yeh, Y.R., Huang, S.Y. and Lee, Y.J. (2009). Nonlinear dimension reduction with kernel sliced inverse regression. IEEE Trans. Knowledge and Data Engineering, 21, 1590-1603.

21. Huang, S.Y. Yeh, Y.R. and Eguchi, S. (2009). Robust kernel principal component analysis. Neural Computation, 21, 3179-3213.

22. Chen, P.C., Huang, S.Y., Chen, W.J. and Hsiao, C.K. (2009). A new regularized least squares support vector regression for gene selection. BMC Bioinformatics, 10:44.

23. Huang, C.M., Lee, Y.J., Lin, D. and Huang, S.Y. (2007). Model selection for support vector machine via uniform design. Computational Statistics and Data Analysis, 52, 335-346.

24. Lee, Y.J. and Huang, S.Y. (2007). Reduced support vector machines: a statistical theory. IEEE Trans. Neural Networks, 18, 1-13.

25. Wang, C., Tsai, M.Y., Lee, M.H., Huang, S.Y., Kao, C.H., Ho, H.N. and Hsiao, C.K. (2007).Maximum number of live births per donor in artificial insemination. Human Reproduction, 22, 1363-1372.

26. Hsiao, C.K., Huang, S.Y. and Chang, C.W. (2004). Bayesian marginal inference via Candidate's formula. Statistics and Computing, 14, 59-66.

27. Lin, M.H, Huang, S.Y. and Chang, Y.C. (2004). Kernel-based discriminant techniques for educational placement. J. Educational & Behavioral Statistics, 29, 219-240.

28. Huang, S.Y., Hsiao, C.K. and Chang, C.W. (2003). Optimal volume-corrected Laplace Metropolis method. Ann. Inst. Statist. Math., 55, 655-670.

29. Lu., H.S., Huang, S.Y. and Lin, F.J. (2003). Generalized cross-validation for wavelet shrinkage in nonparametric mixed-effects models. J. Computational and Graphical Statistics, 12, 714-730.

30. Huang, S.Y. (2002). On a Bayesian aspect for soft wavelet shrinkage estimation under an asymmetric linex loss. Statistics and Probability Letters, 56, 171-175.

31. Huang, S.Y. and Lu, H.S. (2001). Extended Gauss-Markov theorem for nonparametric mixedeffects models. J. Multivariate Analysis, 76, 249-266.

32. Huang, S.Y. and Lu, H.S. (2000). Bayesian wavelet shrinkage for nonparametric mixed-effects models. Statistica Sinica, 10, 1021-1040.

33. Chow, Y.S. and Huang, S.Y. (1999). A characterization of the uniform distribution via moments of n-fold convolution modulo one. Sankhyā A, 61, 148-151.

34. Huang, S.Y. (1999). Density estimation by wavelet-based reproducing kernels. Statistica Sinica, 9, 137-151.

35. Huang, I.C. and Huang, S.Y. (1999). Bernoulli numbers and polynomials via residues. J. Number Theory, 76, 178-193.

36. Huang, S.Y. (1997). Wavelet based empirical Bayes estimation for the uniform distribution. Statistics and Probability Letters, 32, 141-146.

37. Huang, S.Y. and Liang, T. (1997). Empirical Bayes estimation of the truncation parameter with Linex loss. Statistica Sinica, 7, 755-769.

38. Huang, S.Y. (1996). On the consistency of hierarchical Bayes estimators. Statistics and Decisions, 14, 295-305.

39. Huang, S.Y. (1995). Empirical Bayes testing procedures in some nonexponential families using asymmetric Linex loss function. J. Statistical Planning and Inference, 46, 293-309.

40. Huang, S.Y. and Studden, W.J. (1993). Density estimation using spline projection kernels. Communications in Statistics-Theory and Methods, 22, 3263-3285.

41. Huang, S.Y. and Studden, W.J. (1993). An equivalent kernel method for least square spline regression. Statistics and Decisions, supp. 3, 179-201.

Last Update: 2021-05-23  10:56 PM