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Research Interests
My research interests include
Markov chain Monte Carlo
methods, image processing and analysis, pattern
recognition, and clustering and classification.
Education
Ph.D., 2005, Applied Mathematics, Brown University
M.S., 1996, Mathematics, National Taiwan University
B.S., 1994, Mathematics, National Taiwan University
Experience
Associate Research Fellow, 2014-present, Institute of
Statistical Science, Academia Sinica
Assistant Research Fellow, 2006-2014, Institute of
Statistical Science, Academia Sinica
Algorithm Engineer, 2004-2006, Mathematical Technologies Inc, Providence,
U.S.A.
Selected Publications
F. Hsieh*, E. P. Chou, and T.-L. Chen
(2021). "Mimicking
Complexity of Structured Data Matrix’s Information Content: Categorical
Exploratory Data Analysis." Entropy 23, no. 5 (2021): 594.
C.-Y. Hsu*, F. Xiao, K.-L. Liu, T.-L.
Chen, Y.-C. Lee, and W. Wang* (2020). “Radiomic
Analysis of Magnetic Resonance Imaging Predicts Brain Metastases Velocity
and Clinical Outcome After Upfront Radiosurgery,” Neuro-Oncology
Advances, 2(1): 1-13.
T.-L. Chen*, S.-Y. Huang, and W.
Wang (2020). “A
consistency theorem for randomized singular value decomposition”,
Statistics & Probability Letters, 161: 10843 1.
S.-H. Wang*, S.-Y. Huang, and T.-L. Chen
(2020). “On
asymptotic normality of cross data matrix-based PCA in high dimension low
sample size”, Journal of Multivariate Analysis, 175: 104556.
L.-J. Huang, Y.-T. Liao, T.-L. Chen*,
and C.-R. Hwang (2018). “Optimal
variance reduction for Markov chain Monte Carlo,” SIAM Journal on
Control and Optimization, 56(4): 2977–2996.
C.-H. Wu and T.-L. Chen* (2018). “On
the asymptotic variance of Markov chain Monte Carlo with tree structure”,
Statistics & Probability Letters, 137:224-228.
Y.-S. Chin and T.-L. Chen* (2016).
“Minimizing
variable selection criteria by Markov chain Monte Carlo,”
Computational Statistics, 31(4): 1263-1286.
T.-L. Chen*, H. Fujisawa, S.-Y.
Huang and C.-R. Hwang (2016).
“On
the weak convergence and central limit theorem of blurring and nonblurring
processes with application to robust location Estimation,” Journal of
Multivariate Analysis, 143: 165-184.
S.-Y. Shiu and T.-L. Chen* (2016).
“On
the strengths of the self-updating process clustering algorithm,” Journal
of Statistical Computation and Simulation, 86(5): 1010-1031.
S.-Y. Shiu and T.-L. Chen* (2015).
“On
the rate of convergence of the Gibbs sampler for the 1-D Ising model by
geometric bound,”
Statistics & Probability Letters, 105:
14-19.
T.-L. Chen (2015). “On
the Convergence and Consistency of the Blurring Mean-Shift Process,”
Annals of the institute of
Statistical Matheematics.67(1): 157-176.
T.-L. Chen, D.-N.
Hsieh, H. Hung, I-P. Tu*, P.-S. Wu, Y.-M. Wu, W.-H. Chang* and S.-Y. Huang
(2014). “γ-SUP:
a clustering algorithm for cryo-electron microscopy images of asymmetric
particles,”
Annals of
Applied Statistics,
8(1): 259-285.
T.-L. Chen* and S.
Geman (2014). “Image
warping using radial basis functions,” Journal of Applied Statistics,
41(2): 242-258.
T.-L. Chen (2013),
“Optimal
Markov chain Monte Carlo sampling.”
WIREs Comp Stat.
5(5): 341-348.
T.-L. Chen* and
C.-R. Hwang. (2013). “Accelerating
reversible Markov chains.”
Statistics & Probability Letters, 83(9): 1956-1962.
T.-L. Chen*, W.-K. Chen, C.-R. Hwang and H.-M. Pai (2012).
“On
the optimal transition matrix for MCMC sampling.”
SIAM Journal on Control and
Optimization, 50(5): 2743-2762.
T.-L.
Chen and S. Geman* (2008). “On the Minimum Entropy of a Mixture of Unimodal and
Symmetric Distributions,”
IEEE Tran. Information Theory, 54(7): 3166-3174.
T.-L.
Chen* and S.-Y. Shiu (2007). “A New Clustering
Algorithm Based on Self-Updating Process,” In JSM
Proceedings, Statistical Computing Section, Salt Lake City, Utah;
American Statistical Association, pp. 2034-2038.
A. Amarasingham,
T.-L. Chen, S. Geman, M. Harrison
and D. Sheinberg (2006). “Spike
Count Reliability and the Poisson Hypothesis,”
The
Journal of Neuroscience 26(3): 801-809.
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