I am a fifth-year PhD student in Department of Statistics at Columbia University. I am advised by Professor Andrew Gelman. Before coming to Columbia I obtained my undergraduate education from Tsinghua University, where I studied Mathematics.
I anticipate to graduate in early 2021.
My general research interest lies in Bayesian statistics and machine learning. My recent research involves:
- Uncertainty in M-open world: how to do model averaging and model evaluation when the models are wrong, cross validation and marginal likelihood, when these model evaluation methods per se are valid and how to remedy.
- Reliable inference and computation: how to diagnose variational inference and how to improve, metastability in MCMC sampling algorithms, importance sampling and normalization constant.
- Better inference through a predictive lens: Bayesian procudure is coherent but not automatically optiaml. To what extent should we rely on Bayes posterior; can we make better probabilistic predicion from the model that is not even believed to be true?
I am also interested in applying Bayesian methods to real data problems. Projects that I have been involved inclue replication crisis in psychology, groundwater arsenic in South Asia, soil lead after Notre Dame fire, free energy computation, deep Bayes net on imagenet, and Covid-19 predictions.
Yuling Yao, Aki Vehtari, Andrew Gelman.  Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of
Multimodal Posteriors. under review.
[Online] [Blog] [Code]
[Online] [Code] [R package]
Alexander van Geen, Yuling Yao, Tyler Ellis, Andrew Gelman.  Fallout of Lead over Paris from the 2019 Notre-Dame Cathedral Fire. Geohealth, in presss.
[Online] [Code] [Media coverage] [Media coverage 2]
Maarten Marsman, Felix D Schönbrodt, Richard D Morey, Yuling Yao, Andrew Gelman, Eric-Jan Wagenmakers  A Bayesian bird's eye view of ‘Replications of important results in social psychology’. Royal Society Open Science,4,160426.