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Yuling Yao joined the University of Texas at Austin in 2024 as an Assistant Professor in the Department of Statistics and Data Sciences. She previously worked as a research fellow at the Flatiron Institute. Her research primarily focuses on Bayesian methodology, model evaluation averaging, and scalable computation in statistics. She has interests in Markov Chain Monte Carlo (MCMC), variational inference, and simulation-based inference. Her work applies statistical modeling techniques to various fields, including environmental health, social science, and physics. Yao's expertise encompasses a range of methodologies within Bayesian statistics and statistical computing, specifically emphasizing Monte Carlo methods and machine learning approaches.
Flatiron Institute •
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