Thorough knowledge of machine learning techniques for building predictive classifiers, including logistic regression methods, random forest, neural networks, on multi-modal data, including mass spectrometry spectra, single cell sequencing, B cell repertoire sequencing, phage immunoprecipitation, yeast display, and similar. Demonstrates proficiency with modern machine learning toolkits and approaches for classification tasks using large scale datasets, including transcriptomics, and proteomics, demonstrated proficiency using, evaluating, and optimizing protein modeling and folding approaches, including Rosetta, AlphaFold3, and others.