The Genomics team in Google AI develops methods to help understand genomic data and combine it with non-genomic biomedical data. Our team builds DeepVariant, a highly accurate deep learning variant caller. The real world applications are in improving clinical genomics, drug discovery, and in non-human biotechnology. Andrew shapes product strategy to drive adoption of the technology and ensure its connection to high-value application. His role has a strong focus on genomics community engagement, collaboration, and partnership. Prior to Google, Andrew was Chief Scientific Officer at DNAnexus, where he grew and led a team of bioinformaticians who supported many of the first large-scale genomics projects, such as the CHARGE Consortium, Regeneron-Gesinger and Regeneron-UKBiobank cohorts, the 3000 Rice Genomes Project, PrecisionFDA, and the St. Jude Pediatric Cancer Cloud. Andrew holds a PhD in Molecular Biology from Stanford University and a Bachelor’s degree in Physics from the University of Virginia.
AI and Data Sciences Showcase: Google AI
Google AI is focused on bringing the benefits of AI to everyone. The genomics team in Google Brain develops deep learning applications than understand genomic data in the context of broader biomedical knowledge.
DeepVariant: For Highest Accuracy, Extensibility; Training > Programming
DeepVariant demonstrates how deep learning enables a fundamentally different type of development – empowering domain expertise through data. We show four demonstrations of how DeepVariant was adapted for highest accuracy on new datatypes: PCR+ WGS, exomes, BGISEQ, and Pacbio CCS by training with new examples instead of writing new code.