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S. C. Samuel Kou
Professor of Statistics
Research Interests
- Stochastic inference in single molecule biophysics, chemistry and biology
- Bayesian inference of stochastic models
- Nonparametric methods, model selection and empirical Bayes
- Monte Carlo methods
- Economic and financial modeling
Education
- Ph.D. in Statistics, Stanford University, June 2001.
- M.S. in Statistics, Stanford University, March 2000.
- B. S. in Computational Mathematics, Peking University, July 1997.
Experience
- July 2008 -- now, Professor of Statistics, Harvard University
- July 2005 -- June 2008, John L. Loeb Associate Professor of the Natural
Sciences, Harvard University
- July 2001 -- June 2005, Assistant Professor of Statistics,
Harvard University
- September 1997 -- June 2001, Teaching Assistant and Instructor,
Department of Statistics, Stanford University
Sample Publications
- Samuel Kou (2008).
Stochastic
modeling in nanoscale biophysics: subdiffusion within proteins.
Ann. Appl. Statist., 2, 501-535.
- Samuel Kou, Qing Zhou and Wing Wong (2006).
Equi-energy
sampler with applications in statistical inference and statistical mechanics (with discussion).
Ann. Statist., 34, 1581-1652.
- Samuel Kou, Sunney Xie and Jun Liu (2005).
Bayesian analysis of
single-molecule experimental data (with discussion).
J. Roy. Statist.
Soc., C, 54, 469-506.
- Samuel Kou, Binny Cherayil, Wei Min, Brian
English and Sunney Xie (2005). Single-molecule Michaelis-Menten equations .
Journal of Physical Chemistry, B, 109, 19068-19081.
- Samuel Kou (2004). From finite sample to asymptotics: a
geometric bridge for selection criteria in spline regression.
Ann. Statist.,
32, 2444-2468.
- Samuel Kou and Sunney Xie (2004).
Generalized Langevin equation
with fractional Gaussian noise: subdiffusion within a single protein molecule.
Physical Review Letters, 93, 180603(1)-180603(4).
- Samuel Kou and Steve Kou (2003).
Modeling growth stocks via
birth-death processes. Advances in Applied Probability, 35, 641-664.
- Samuel Kou and Bradley Efron (2002).
Smoothers and the Cp,
GML and EE criteria: a geometric approach. J. Amer. Statist. Assoc.,
97, 766-782.
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