Daniel Neil is a machine learning researcher who is passionate about bringing transformative technologies to the world. After a foundation in biomedical computation at Stanford, Daniel worked as a technology consultant with Accenture in Silicon Valley before obtaining a Ph.D. in Switzerland at ETH Zurich in machine learning and neuroscience.  He is the author of more than three dozen publications and patents in research areas spanning biologically-motivated machine learning, algorithm development, and knowledge graph completion. At BenevolentAI, Dan helped to build the New York office's research team and direction, and now focuses on integrating research teams across information extraction, precision medicine, gene prioritization, and chemistry optimization to deploy machine learning algorithms that improve each step of the drug discovery process.