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, cancer natural history modeling and vaccine trials. 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 Bayesian 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: Min Chen and Dr. Jason Xu,
	Detecting changes in the transmission rate of a stochastic epidemic model Presented at the 2022 Joint Statistical Meeting Collaborator: Jenny Huang, Prof. David Dunson and Dr. Jason Xu arXiv (accepted for publication by Statistics in Medicine)
	A Bayesian approach for fitting semi-Markov mixture models of cancer latency to individual-level screens Applications: cancer natural history modeling and overdiagnosis rate estimation Presented at EcoSta 2023 Collaborators: Dr. Marc Ryser, Dr. Shannon Holloway 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 Collaborator: Dr. Alexander Fisher Course website
	Content-based course recommender system for liberal arts education Collaborators: Sofia Vazquez Alferez and Dr. Evgueni Smirnov Presented at the 2019 International Conference on Educational Data Mining Morsomme, R., & Alferez, S. V. (2019). Content-Based Course Recommender System for Liberal Arts Education. International educational data mining society.
	Conformal prediction for students’ grades in a course recommender system Collaborator: Dr. Evgueni Smirnov Presented at the 2019 Symposium on Conformal and Probabilistic Prediction and Applications Morsomme, R., & Smirnov, E. (2019, August). Conformal prediction for students’ grades in a course recommender system. In Conformal and Probabilistic Prediction and Applications (pp. 196-213). PMLR.