Zafer Barutcuoglu will present his preFPO on Thursday February 28 at 1:30 PM in Room 402. The members of his committee are: Rob Schapire (advisor); Olga Troyanskaya and David Blei (readers); Mona Singh and Fei-Fei Li (non-readers). Everyone is invited to attend his talk. His abstract follows below. ---------------------------------- Classification problems encountered in real-life applications often have domain-specific structural information available on the measured data, which cannot be readily accommodated by conventional machine learning algorithms. Ignoring the structure and blindly running a conventional algorithm on the numerical data can compromise the quality of solutions. This thesis provides answers to two such complementary settings; one where there is a hierarchy among multiple class labels (output structure), and one where the input features are known to be sequentially correlated (input structure). Probabilistic graphical models are used to encode the dependencies, and efficient Bayesian inference algorithms are used for parameter estimation. While both scenarios are motivated by real bioinformatics problems, namely gene function prediction and aneuploidy-based cancer classification, they have applications in other domains as well, such as computer graphics, music, and text classification.