**Leo Grady (Siemens Corporate Research)
**

An ideal segmentation algorithm could be applied equally to the problem of isolating organs in a
medical volume or to editing a digital photograph without modifying the algorithm, changing parameters,
or sacrificing segmentation quality. However, a general-purpose, multiway segmentation of objects in an
image/volume remains a challenging problem. In this talk, I will describe a recently developed approach
to this problem that inputs a few training points from a user (e.g., from mouse clicks) and produces a
segmentation by computing the probabilities that a random walker leaving unlabeled pixels/voxels will
first strike the training set. By exact mathematical equivalence with a problem from potential theory,
these probabilities may be computed analytically and deterministically. The algorithm is developed on an
arbitrary, weighted, graph/mesh in order to maximize the broadness of application. I will illustrate the
use of this approach with examples from several segmentation problems (without modifying the algorithm or
the single free parameter), compare this algorithm to other approaches and discuss the theoretical
properties that describe its behavior.

Graph kernels and applications in chemoinformatics

**Jean-Philippe Vert (Centre for Computational Biology, Ecole des Mines de Paris)**

Several problems in chemistry can be formulated as classification or regression problems
over molecules which, when represented by their planar structure, can be seen as labeled
graphs. Several approaches have been proposed recently to define positive definite kernels
over labeled graphs, paving the way to the use of powerful kernel methods in chemoinformatics.
In this talk I will review some of these approaches and present relevant applications
in computational chemistry.