We developed the extended Projection Pursuit Regression (ePPR) algorithm that, by using an efficient projection pursuit estimation algorithm, allows to estimate a very general linearn-nonlinear model of visual cells using arbitrary, including natural, stimuli.

We introduced the Volterra Relevant Space Technique to estimate spatial Volterra models of visual cells using natural images.

We developed a system for 3D PET image reconstructions using a statistically optimal maximum a posteriori (MAP) reconstruction algorithm. To speed up the reconstruction process we implemented a pararellized version of the reconstruction algorithm, and run the reconstructions on a cluster of personal computers. To allow scanning facilities remotely access our reconstruction system, we developed a Java interace to the cluster that only requires a standard Web browser to run 3D PET image reconstructions.

I developed a technique that learned from a training collection of HTML documents the optimal way to combine several HTML ranking heuristics into a single similarity measure for HTML documents.