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 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.