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TITLE: Stochastic inference of dynamic system models: from single-molecule experiments
to statistical estimation
SPEAKER: Samuel Kou
ABSTRACT:
Dynamic systems, often described by coupled differential equations, are used in modeling diverse behaviors in a wide variety of scientific disciplines. In this talk we will consider their assessment and calibration in light of experimental/observational data. For the assessment, we explore how the deterministic dynamic system models reconcile with stochastic observations, using recent single-molecule experiments on enzymatic reactions as an example, where the single-molecule data reveal clear departure from the classical
Michaelis-Menten model. For the calibration, we will propose a new inference method for the parameter estimation of dynamic systems. The new method employs Gaussian processes to mirror a dynamic system and offers large savings of computational time while still retains high estimation accuracy. Numerical examples will be used to illustrate the estimation method.
Bio:
Samuel Kou is Professor of Statistics at Harvard University. He received a bachelor's degree in computational mathematics from Peking University in 1997, followed by a Ph.D. in statistics from Stanford University in 2001 under the supervision of Professor Bradley Efron. After completing his Ph.D., he joined Harvard University as an Assistant Professor. He was promoted to full professor in 2008.
His research interests include stochastic inference in single-molecule biophysics, chemistry and biology; Bayesian inference for stochastic models; nonparametric statistical methods; model selection and empirical Bayes methods; Monte Carlo methods; and economic and financial modeling. He won the prestigious COPSS award. He is a fellow of ASA, and so on.
