Weilun's research focuses on how individuals adaptively choose different learning and decision-making strategies when facing uncertainties in a dynamic environment. He uses behavioral measures, computational models, and human fMRI to elucidate the underlying algorithmic and neural mechanisms. Weilun received his B.A. in Economics at Fudan University in Shanghai and his M.S. in Data Science at the University of Rochester in New York. He worked as a research assistant at the Neuroeconomics Lab at UC Berkeley before joining Caltech.