My interests as a statistician cover multivariate analysis and regression (including dimension reduction, supervised and unsupervised classification, non-parametric tools and parametric regression modeling), computational techniques (including re-sampling, perturbation and permutation schemes for the empirical assessment of significance), approaches to the analysis of high-dimensional and under-sampled data (which are very common in genomics applications), and Markov models (as applied to genomic sequences and alignments). With funding from the NSF, R. Dennis Cook (Statistics, U of MN), Bing Li (Statistics, Penn State) and I have pursued research on *Sufficient Dimension Reduction*, a body of theory and methods for handling high-dimensional regression and classification problems, which is also closely related to graphics and data visualization.

**REPRESENTATIVE PUBLICATIONS**

- Lee K.Y., Li B. and Chiaromonte F. (2013) A general theory of nonlinear sufficient dimension reduction: formulation and estimation.
*Annals of Statistics***41**(1), 221-249. doi:10.1214/12-AOS1071 - Chiaromonte F. and Taylor J. (2010) Information Based Agglomerative Segmentation in Metric Spaces.
,*Journal of the Indian Society of Agricultural Statistics***64**(1), 33-44. - Cook R.D., Li B. and Chiaromonte F. (2010) Envelope models for parsimonious and efficient multivariate linear regression. Discussion paper.
,*Statistica Sinica***20**(3), 927-910 (including comments and rejoinder). - Tyekucheva S. and Chiaromonte F. (2008) Augmenting the bootstrap to analyze high dimensional genomic data (invited article with discussion).
,*Test***17**, 1-18 (article), 47-55 (rejoinder). - Cook R.D., Li B. and Chiaromonte F. (2007) Dimension reduction in regression without matrix inversion.
,*Biometrika***94**, 569-584. - Li B., Zha H. and Chiaromonte F. (2005) Contour regression: a general approach to dimension reduction.
,*Annals of Statistics***33**, 1580-1616. - Li B., Cook R.D. and Chiaromonte F. (2004) Dimension reduction for the conditional mean in regressions with categorical predictors.
,*Annals of Statistics***30**, 1636-1668. - Chiaromonte F., Cook R.D. and Li B. (2002) Sufficient dimension reduction in regressions with categorical predictors.
,*Annals of Statistics***30**, 475-497 - Chiaromonte F. and Cook R.D. (2002) Sufficient dimension-reduction and graphics in regression.
,*Annals of the Institute of Statistical Mathematics***54**, 768-795. - Chiaromonte F. and Martinelli J.A. (2002) Dimension reduction strategies for analyzing global gene expression data with a response.
,*Mathematical Biosciences***176**, 123-144.