Natalie Eyke received her Bachelor of Science in Engineering degree in Chemical Engineering from the University of Michigan and her Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology. Before attending graduate school, she spent three years as a scientist in the Chemical Engineering R&D department at Merck in Rahway, NJ, where she supported late-stage drug substance process development and helped author the NDA filing for Doravirine. When she transitioned to graduate school, her experience at Merck inspired her research objectives, and she focused on developing a series of tools designed to accelerate pharmaceutical process development. In her first year at MIT, she constructed machine learning models capable of predicting reaction yields. Next, she transitioned to a hardware focus: in collaboration with colleagues at MIT and sponsors at Pfizer, she designed, constructed, and demonstrated the capabilities of a fully-automated reaction screening platform linked to a Bayesian optimizer that is designed to improve high-throughput reaction screening efficiency by allowing the conditions of reactions performed in parallel to be totally independent and selected freely by the Bayesian optimizer. After defending her thesis, she joined the small molecule drug substance process development group at Vertex Pharmaceuticals in Boston, MA, where she remains enthusiastic about applying new machine learning and automation tools to challenges in pharmaceutical process development.