My research program sits at the intersection of statistics, biology and medicine. My statistical methodology interests are mainly in high-dimensional/Big data modeling and Bayesian inference. This includes Bayesian bioinformatics, functional data analyses, graphical models, Bayesian semi-/non parametric models and Bayesian machine learning. These methods are motivated by modern biomedical technologies generating large and complex-structured datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. A special focus is on developing integrative models combining different sources of biomedical big data for biomarker discovery and clinical prediction to aid precision/translational medicine.

Through the course of my career, I have developed a broad perspective of applied scientific problems, leveraging the underlying scientific hypotheses to be the motivating factors for development of new statistical and computational methodologies. An overarching goal of my research plan is to provide accurate probabilistic representations of applied problems using novel data-driven, robust and flexible statistical methods, incorporating all sources of knowledge from the substantive area of investigation – to achieve impactful scientific results.