Classification and Regression with High-dimensional Measurements
There is an increasing demanding for efficient and accurate classification and regression algorithms based on high-dimensional features (e.g. hyperspectral data generated by remote sensing technology, gene expression data). I am interested in developing Bayesian methodologies to model the high-dimensional variables, in order to find a better predictive distribution of the response given the high-dimensional variables.
Detecting Differential Variables from High-throughput Data
It is crucial to find differential variables from high-throughput data (e.g. gene expression data, mass spectrometry data) for further laboratory experiments. There is considerable correlations between these high-dimensional data. Considering the correlations in finding differential variables is crucial but challenging. I am interested in developing Bayesian methodologies to model the correlations.
Classification and Regression with High-order Interactions
A response variable (e.g. a certain disease) may be related to high-order interactions of a set of covariates (e.g. interactions between genes and environmental exposures). I am interested in using Bayesian methodologies to model this relationship and solving the arising computational problems.
Modelling DNA sequences
It is hypothesized that there is long-range dependency among DNA sequences. Modelling this dependency is crucial in many problems in bioinformatics, such as dicovering transcription-factor binding sites and motifs, and halplotype inference. I am interested in developing Bayesian methodologies to model this dependency.
Key words to summarize my research
Bayesian Classification and Regression, Monte Carlo Methods, Machine Learning, Bioinformatics
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