My academic interests revolve around the development of efficient algorithms for Bayesian inference for problems characterized by high-dimensional missing data. My work has found applications in contagious disease modelling, oncology and clinical studies. Before my Ph.D., I developed a content-based course recommender system for students of a Liberal Arts college that was based on a topic model of course content and conformal prediction of course grades.

Exact inference for stochastic epidemic models via uniformly ergodic block sampling

  • Recipient of the Young Investigator Award, ASA Section on Statistics in Epidemiology
  • Presented at the 2021 Joint Statistical Meeting
  • Collaborator: Dr. Jason Xu
  • arXiv, R package

A flexible and scalable class of time-varying stochastic epidemic models

  • Presented at the 2022 World Meeting of the International Society of Bayesian Analysis
  • Collaborator: Dr Jason Xu, Dr. Raphaelle Klitting, Dr. Andrew Hollbrook

Detecting changes in the transmission rate of a stochastic epidemic model

  • Collaborator: Jenny Huang, Prof. David Dunson, Dr. Jason Xu
  • arXiv

Data-augmentation Markov chain Monte Carlo for fitting semi-Markov breast cancer models to mammograms

  • Applications: estimation of overdiagnosis rate and natural history estimation
  • Collaborators: Dr. Marc Ryser and Dr. Jason Xu

A data assimilation framework for assessing treatment efficacy with multistate semi-Markov models

  • Collaborators: Dr. Jon Fintzi and Dr. Jason Liang

BEDD – Binary event driven design

  • Collaborators: Dr. Erica Brittain and Dr. Michael Proschan

Scalable dynamic Bayesian system for long-term forecasting of high-dimensional macroeconomic time series

  • Collaborators: Dr. Jun Jiang and Yun Ji

Designing an introductory statistics class for an accelerated summer session

Content-based course recommender system for liberal arts education

Conformal prediction for students’ grades in a course recommender system