Shawn McKay is a senior engineer at RAND. McKay's current research has focused on artificial intelligence applications in decision making. Applications of using machine learning to develop predictive and prescriptive models to help the Army understand materiel readiness, soldier retention, and UAV mishaps underlying drivers have been developed under McKay’s leadership. At RAND, McKay has also led research in a variety of areas including business intelligence, quality management, autonomous vehicle technology, acquisition policy, and logistics. Notable projects include identified proactive ways to reduce risk of UAV mishaps and improve materiel readiness using machine learning; strengthen the ability to ensure fielded systems are meeting acquisition requirements through data analytics methods; identified acquisition strategies for autonomous vehicle technology; developed business intelligence application for the management of Army personnel equipment inventories and excess management; developed a process model of Army cyber operations to assess resource, technology, process, and policy implications; developed a part quality management tool for early identification of parts causing high quality costs; created metrics and strategies for reducing inventory excess; created a federated model to assess aircraft survivability in contested airspace; conducted acquisition policy research with emphasis on testing and fielding emergent cyber technologies; assessed aircraft component reliability with mixed-Weibull and Crow-ASMAA methodologies to determine effectiveness of part redesigns; assessed software reuse potential and organizational limitations for Navy system oriented architecture efforts; and contributed to/conducted verification and validation studies on Homeland Security and Army network models. McKay has a Ph.D. in engineering from Purdue University.