Computational Biology

Recent advances in biotechnology have produced a wealth of genomic data, which capture a variety of complementary cellular features. While these data promise to yield key insights into molecular biology and medicine, much of the information present remains underutilized because of the lack of scalable approaches for detecting signals across large, diverse data sets. A proper framework for capturing these numerous snapshots of complementary phenomena under a variety of conditions can provide the holistic view necessary for developing relevant and precise systems-level hypotheses of new drug targets and disease mechanisms.

My research is concerned with developing statistical and mathematical models of complex biological systems and analyzing large-scale molecular data. My research interests range from the analysis of microarray data in clinical settings to inference of cellular networks from high-throughput gene perturbation screens and integration of heterogeneous data sources using machine learning techniques and probabilistic graphical models.