My primary field of research interest is AI with focus on problems of
planning, reasoning, optimization and learning in the
presence of uncertainty. More specifically, I am interested in and want to extend our ability
to model and solve complex high-dimensional learning and decision-making problems
with stochastic components by investigating and developing new modeling frameworks allowing us to better represent special
domain features and structure, and efficient algorithmic solutions
operating upon such models. My inter-disciplinary work and research is in
biomedical informatics.
Markov decision processes. Design of
efficient value-function and policy approximation methods for solving
large MDPs. Extensions of MDPs to continuous time, continuous state
and partially observable settings.
Bioinformatics. Statistical methods for identification of hidden regulatory pathways
from gene expression data. Algorithms for preprocessing and analysis of high-throughput MS proteomic and genomic data and for biomarker discovery.
New methods for protein ID in whole sample proteomics using prior knowledge
