Yuling Yao joined the Center for Computational Mathematics in July 2021. Yao’s research consists of developing a scalable Bayesian workflow for real data problems. Past applications include the lead fallout in Paris, arsenic contamination in Bangladeshi groundwater, and forecasting United States elections. To improve methodology development in applied statistics, Yao also investigates statistical and machine learning methods for model evaluation, selection, and aggregation. Currently, Yao focuses on cross-validation, stacking, and Bayesian causal inference. Lastly, Yao develops algorithms for full Bayesian computation and its approximation. Yao is working on combining Monte Carlo methods with sophisticated numerical tricks, including regularized importance sampling, simulated tempering and annealing, and free energy estimation. Yao holds a Ph.D. in Statistics from Columbia University and a B.S. in Mathematics from Tsinghua University.