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now. I develop statistical and computational elements for the analysis of complex graphs and interacting dynamical systems, including yeast molecular biology and social networks. The focus of my research is on (i) statistical methodology and theory for graphs with rich node information, including the exchangeable graph model, (ii) metabolism and cellular proliferation, (iii) hypotheses testing and inference on signaling and metabolic pathways, including the map-kinase pathways, and the pathways regulating carbon and nitrogen metabolism. More broadly, my interests include probabilitsic algorithms, approximation theorems, random matrix analysis, convex and combinatiorial optimization, and geometrical intuitions.
brief bio. In December 2006, I receved a Ph.D. from Carnegie Mellon, working on statistical machine learning and the analysis of complex systems with Stephen Fienberg and Kathleen Carley. My dissertation introduced statistical and computational elements of graph theory that support data analysis of complex systems and their evolution. Till December 2008, I was a postdoctoral fellow in the Lewis-Sigler Institute for Integrative Genomics of Princeton University working with Olga Troyanskaya, David Botstein, and James Broach. I developed mechanistic models to gain computational insights into aspects of the molecular and cellular biology that are not directly observable with experimental probes. I have been working closely with biologists and in the areas of cellular differentiation, cellular development and cancer, since.