Sheng Wang

Sheng Wang’s work focuses on applying data science techniques to provide evidence-driven, fast-turnaround solutions for a variety of federal, state, and commercial clients.

Since joining Mathematica in 2011, Wang has worked on a range of projects in education, labor, and health. He is currently directing a project focused on developing a machine-learning and artificial-intelligence engine that analyzes patients’ conditions, treatments, and other relevant factors to optimize pricing. The engine will provide the foundation for creating new fixed-price packages that will support payer engagement, ensuring a sustainable and profitable payment model for the future. Wang has served as the chief statistician for Hospital Quality Indicator and Area Quality Indicator projects, leading efforts to use data from the Healthcare Cost and Utilization Project to create risk- and reliability-adjusted hospital and area quality indicators. He has also developed data science tools such as matching, measure reliability, and statistical modeling to support evidence-based policymaking and real-time decision making for federal, state, and commercial clients.

Before joining Mathematica in 2011, Wang honed his statistics and machine-learning skills as a graduate student, teaching assistant, and intern for Eli Lilly and Company. His research involved analysis of nonignorable missing data, statistical inferences, and high-dimensional data analysis and feature selection. He holds a Ph.D. in statistics from the University of Wisconsin-Madison.