About Yuling Yao

Starting in 2024, I am a tenure-track Assistant Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin.

Before UT, I spent three years as a Flatiron Research Fellow at the Flatiron Institute, Center for Computational Mathematics. Before Flatiron, I earned my Ph.D. in Statistics from Columbia University in 2021 under the supervision of Andrew Gelman. Before that, I obtained my undergraduate education from Tsinghua University in mathematics and economics.

About my research

I develop scalable Bayesian methods for applied data problems, with a strong emphasis on probabilistic modeling and uncertainty quantification. My recent applications span diverse areas, including lead fallout in Paris, arsenic contamination in Bangladeshi groundwater, the evolution of the Universe post-Big Bang, and bottom quark tagging in particle collider experiments.

But better applied statistics needs better methodology. To that end, I design statistical and machine learning methods, with a focus on model evaluation, aggregation, causal inference and inference under misspecification. Some ongoing progresses are on cross-validation, stacking and hierarchical stacking, and covariate imbalance.

But complex methods further need scalable and diagnosable computing. Hence, I develop algorithms and theories for fully Bayesian and approximate computations, including importance sampling, simulated tempering and annealing, and multimodal MCMC sampling. My current interest is on modern simulation-based and score-based methods.