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I am a data scientist with a quantiative background and formal training in electrical and computer engineering and a huge passion for biological problems that data can help solve. Since grad school, my work has been at the intersection of computational biology, applied statistics and machine learning, and exploratory data science. My projects typically start by gaining an in-depth understanding of a biological problem and the related data, and then inferring novel insights by developing new algorithms and/or using existing machine learning and statistical tools. I have experience working with different types of data including immune repertoire sequencing data, clinical data, wet-lab assay data, and metabolomics data (NGS, tabular, network data) and I am always fascinated by new biological problems that we can potentially help solve using data and machine learning. For more information on my research and background, see more about me .

I use this site to write blogs related to concepts in machine learning and data science using very simple experiments and visualizations. Check them out here.