Introduction by Dr. E. Belitser, TU/e
 

Modern data come in the form of all possible complexities. Due to astonishing advance of technology in the recent years, new forms of data such as functions, images, curves, shapes, trees or extremely high dimensional arrays are being encountered and their statistical analysis pose new challenges. Testing of many hypotheses simultaneously is a new statistical problem which arise with the huge amount of data collected  from genomic and brain imaging experiments. Understanding these complex data objects and extracting information from them require finding meaningful structures described by lower dimensional characteristics which are hidden under the pile of less useful information. The Bayesian approach offers probabilistic modeling of those characteristics in the form of a prior distribution and update the information in the light of the data using Bayes' theorem. The prior distribution is an effective tool of describing any qualitative characteristic such as clustering of similar data in hidden groups. Breakthroughs in computational techniques together with exponentially increasing computing power allow Bayesian computations to be performed. We shall describe stochastic processes such as the Chinese restaurant process and the Indian buffet process as prior distribution for feature sharing and sparsity, and discuss their use in processing of image and functional data. We shall also discuss new Bayesian methods for simultaneous testing of a large number of hypotheses.

Research Interests
Nonparametric Bayesian inference, multiple hypothesis testing, high dimensional data, ROC analysis, Bayesian imaging, functional data analysis, noninformative priors, asymptotic properties of the posterior distributions, nonregular cases, Bayesian computation, nonparametric regression, recur-
rent event data, survival analysis, limit theorems in probability.