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-seq data (including quality control, normalization, dimensionality reduction, clustering, cell-type annotation, differential expression, pathway analysis, and comparison of cellular states across
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, clustering, classification or chemometric methods. Machine learning/AI approaches for spectroscopy, imaging or other high-dimensional experimental datasets. Development and automation of optical
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implementation of statistical and machine learning models to analyze high-dimensional biological data, including classification, clustering, feature selection, and predictive modeling. Rutgers Cancer
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experience includes, but is not limited to application, infrastructure, database, and networking architecture; large scale production Windows environments; Active Director, GPO, DNS, Clustering, and Windows