I received my M.S. (2006) and Ph.D. (2012) in Computational Biology from New York University studying the condition-specific usage of functional models in C. elegans development. While there I was able to study additional questions related to machine-learning of phenotypes in mouse early embryonic development, network analysis in Drosophila reproduction and human DNA damage response, and characterize dynamic protein localization patterns in C. elegans embryogenesis.
At IBM, I am interested in network analysis/inference, machine learning, and challenge-based approaches for answering complex biological questions. I have worked to develop Watson Genomics, a system designed to support clinical oncologists make better treatment decisions by performing a genomic analysis of the patient and recommending therapies that are specific to the alterations they possess. This work leverages expertise from across domains - cancer biology, cell biology, machine learning, network analysis, natural language processing - and is being offered as a cloud-based service that reduces what was formally a weeks-long manual analysis of a patient's genomic profile to a minutes-long analysis. Currently, and in collaboration with the Broad Institute, we are studying mechanisms of drug resistance across a wide array of treatments and cancer types in a massive multi-omics study.