I'm now in my 5th year. I'm interested in mathematical models of higher-order cognition, including Learning, Decision-making, Representation and Inference, etc. Here's my CV. (In case the link sometimes doesn't work, please right click 'Save Link As' to download.)
Currently, I'm studying the adaptive learning and decision making in sequential decision tasks under uncertain environment. I'm interested in developing optimal solutions to these tasks using sensible utilities, and comparing with human behaviors. In the past, I have worked on the projects listed below:
1)Computational models of the bandit problems, with Michael Lee and Mark Steyvers.
One line of my research is to understand human decision-making under uncertainty. We develop simple, psychologically interpretable heuristics which address the trade-off between exploration and exploitation and compare them to the optimal benchmarks from machine learning theories.
From an experimenter's perspective,an interesting problem is to optimize the experimental design which will best discriminate between quantitative models. We solve this problem by adopting simulation-based Bayesian design framework that uses Markov Chain Monte Carlo methods.
2)Mathematical models and statistics for experimental psychology, with Geoff Iverson.
Following Psychological Science's formal introduction of the statistic Prep (Probability of replication) in lieu of the traditional P-value, we try to clarify the definition, meaning, calculation and interpretation of this statistic from a Bayesian perspective.
3)"Wisdom of the Crowds" analysis.
In cognitive tasks that involve sequential actions, we try to aggregate people's knowledge using sensible cognitive models, leading to a nearly-optimal performance.
4) I also did some work with Rich Shiffrin on rational game solutions.
