I am a Bayesian statistician, currently finishing a Ph.D. in statistical science at Duke University under the supervision of Prof. Jason Xu. I successfully defended a thesis titled Exact Bayesian Inference for High-dimensional Latent Variable Stochastic Models with Complex, Discrete Structures in June 2024. My research revolves 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 vaccines trials.
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.
In parallel to my doctoral research, I have interned at the National Institute of Allergy and Infectious Diseases, NIH, since May 2023. I have analyszed event-based trial designs for binary endpoints, and collaborated on the developement of the Julia package MultistateModels that fits semi-Markov multistate models to panel data using the Monte Carlo EM algorithm.
Finally, I am currently enrolled in 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).