I am a fourth-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.
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.
I am also interested in applying statistical methods to real data, including replication crisis in psychology, arsenic in groundwater, and penumbra of social network.
[Online] [Blog] [Code]
"When I say 'I love you', you look accordingly skeptical."
[Online] [Code] [R package]
"Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian."
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.
"When effect size is tiny and measurement error is huge, you’re essentially trying to use a bathroom scale to weigh a feather —- and the feather is resting loosely in the pouch of a kangaroo that is vigorously jumping up and down."