Due to recent sensory technology advancement, there is a huge surge in metabolomics, proteomics, glycomics, and genomic datasets. This opens the door to large comparative studies and inference that can shed light on the underlying dynamics and facilitators of the many organic processes.
We develop statistical methodologies for studying such data and robustly interpreting the resulting models. The statistical methodology developed is based on advanced computational algorithms, signal processing, machine learning and robust statistics methods.
In particular, we concentrate on time series proteomics and study data size effect on graph model estimation such as causal models and Bayesian models.