Biological Sciences, Social Sciences, Statistics
Applications in Health Sciences
Applications in Life Sciences
Applications in Social Sciences
I am generally interested in developing statistical procedures for analysis of complex systems, including biological and social systems. The main characteristic of these systems is that they are often comprised of large number of interacting components. System components may correspond to genes, proteins and metabolites, in the case of biological systems, and individual players or organizations in case of social systems; the common feature of such systems is that their behavior may not be evident from that of individual components.
My current research is mainly focused on statistical methods for high dimensional network models, which are commonly used to represent interactions among components of complex systems. Towards this goal, I work on developing statistical methods that efficiently incorporate available network information and can also be extended to analyze networks with unknown or partially known interactions. This is an emerging area of research in the intersection of statistical learning theory, network theory and systems biology, with interesting methodological and applied problems in analysis of high dimensional data. I am also interested in developing new method for inference in high dimensional settings, including identification of sparse signal among high dimensional and correlated hypotheses, as well as statistical methods for integrative analysis of diverse omics data sets.
I am always looking for opportunities for collaborations in both methodology development, as well as applications of the above methodologies, and for graduate students (from Biostatistics, Statistics and/or related areas) interested in these problems.