I am a Bayesian statistician. I recently completed a Ph.D. in statistical science at Duke University under the supervision of Prof. Jason Xu. My thesis was titled Exact Bayesian Inference for High-dimensional Latent Variable Stochastic Models with Complex, Discrete Structures. My doctoral research revolved around the development of efficient sampling algorithms for Bayesian inference and maximum likelihood estimation for problems characterized by high-dimensional, complex latent variables. My work has found applications in contagious disease modeling, cancer natural history modeling and vaccine trials.
During my Ph.D., I spent 15 months at the National Institute of Allergy and Infectious Diseases, NIH, as a visiting researcher. I studied event-based trial designs for binary endpoints, and collaborated on the development of the Julia package MultistateModels that fits semi-Markov multistate models to panel data using the Monte Carlo EM algorithm.
I have obtained a Masters in statistical science at Duke University and a double B.S. in Liberal Arts at the University College Maastricht, the Netherlands, and the University College Freiburg, Germany.
I obtained the Certificate of College Teaching at Duke University where I taught STA101 Data Analysis and Statistical Inference in 2021 and 2022. I have also mentored J. Huang (Major in statistical science and computer science) and M. Chen (Masters in statistical science).