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 am likely to graduate in summer 2020 or early 2021. I tend to avoid point estimation.
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 Bayesian methods to real data problems, including replication crisis in psychology, arsenic in groundwater, and deep learning.
Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry. [2019+] Pareto Smoothed Importance Sampling
. arxiv preprint.
[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.