Kahn Rhrissorrakrai

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 have been able to study questions relating to 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 - to reduce what was formally a weeks-long manual analysis of a patient's genomic profile to a minutes-long analysis.

Most recently, we have been developing new reuseable technologies in cancer drug resistance analysis that have broader applications to other disease areas, including phylogenetics, single cell analysis using TDA, lesion ctDNA shedding, AI driven feature selection, and multimodal data analysis.