2023

  • December 2023: My PhD student Shijie Wang has had the first chapter of his dissertation “Generative multi-purpose sampler for weighted M-estimation” (co-authored with Minsuk Shin and Jun Liu) accepted for publication in Journal of Computational and Graphical Statistics. Paper

  • October 2023: “Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance” (with Mary R. Boland and Yong Chen) has been published in Journal of Machine Learning Research. Paper

  • October 2023: New preprint “Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm” (with Anja Zgodic, Jiajia Zhang, and Alexander McClain). arXiv

  • September 2023: New preprint “Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression” (with Anja Zgodic, Jiajia Zhang, Yuan Wang, Chris Rorden, and Alexander McLain). arXiv

  • June 2023: New preprint “Bayesian group regularization in generalized linear models with a continuous spike-and-slab prior.” arXiv

  • June 2023: I received a Big Data Health Science Center Pilot Project grant to develop new methods for matrix completion with applications to computational drug repositioning.

  • May 2023: New preprint “Two-step mixed-type multivariate Bayesian sparse variable selection with shrinkage priors” (with Shao-Hsuan Wang and Hsin-Hsiung Huang). arXiv

  • May 2023: My undergraduate student Hung-Tien Huang has graduated with his BS in Computer Science from USC and will join University of North Carolina-Chapel Hill this fall for his PhD in Computer Science.

  • April 2023: My PhD student Shijie Wang has won the James D. Lynch Graduate Student Research Award, an award given annually by the USC Statistics Department to one graduate student who has excelled in research.

  • January 2023: I have been selected as a McClausland Faculty Fellow. This selective fellowship supports early-career USC College of Arts and Sciences faculty who are rising stars in their academic disciplines and committed, creative teachers.

2022

  • November 2022: New preprint “Bayesian Modal Regression based on Mixture Distributions” (with Qingyang Liu and Xianzheng Huang). arXiv

  • May 2022: I became the Principal Investigator for NSF grant DMS-2015528 to develop new approaches to deep-learning-based generative models, scalable uncertainty quantification, and Bayesian variable selection.

  • April 2022: I received an ASPIRE-I grant to develop new methods and algorithms for scalable Bayesian survival analysis.

  • March 2022: “Spike-and-slab group lassos for grouped regression and sparse generalized additive models” (with Gemma E. Moran, Joseph L. Antonelli, Yong Chen, and Mary R. Boland) has been published in Journal of the American Statistical Association. Paper

  • January 2022: “Neighborhood deprivation increases the risk of post-induction cesarean delivery” (with Jessica R. Meeker, Heather H. Burris, Lisa D. Levine, and Mary R. Boland) has been published in Journal of the American Medical Informatics Association. Paper

2021

  • December 2021: “Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO” (with Veronika Ročková and Edward I. George) has been published in the Handbook of Bayesian Variable Selection. Paper

  • October 2021: New preprint “A Bayesian selection model for correcting outcome reporting bias with application to a meta-analysis on heart failure interventions” (with Xiaokang Liu, Lifeng Lin, Yulun Liu, Stephen E. Kimmel, Haitao Chu, and Yong Chen). arXiv

  • May 2021: “Individual- and neighborhood-level risk factors for severe maternal morbidity” (with Jessica R. Meeker, Silvia P. Canelón, Lisa D. Levine, and Mary R. Boland) has been published in Obstetrics & Gynecology. Paper

  • May 2021: “Association of neighborhood-level factors and COVID-19 infection patterns in Philadelphia using spatial regression” (with Mary Boland, Jessica Liu, Cecilia Balocchi, Jessica Meeker, Ian Mellis, Danielle L. Mowery, and Daniel Herman) has been published in AMIA Annual Symposium Proceedings. Paper

  • May 2021: New preprint “Uncovering patterns for adverse pregnancy outcomes with spatial analysis: Evidence from Philadelphia” (with Cecilia Balocchi, Jessica Liu, Silvia P. Canelón, Edward I. George, Yong Chen, and Mary R. Boland). arXiv

  • April 2021: “On the beta prime prior for scale parameters in high-dimensional Bayesian regression models” (with Malay Ghosh) has been published in Statistica Sinica. Paper

2020

  • August 2020: I finished my postdoc at the University of Pennsylvania and joined the University of South Carolina Department of Statistics as a tenure-track Assistant Professor.

  • May 2020: New preprint “A robust Bayesian Copas selection model for correcting and quantifying the impact of publication bias” (with Lifeng Lin, Mary R. Boland, and Yong Chen). arXiv

  • March 2020: New preprint “VCBART: Bayesian trees for varying coefficients” (with Sameer K. Deshpande, Cecilia Balocchi, Jennifer E. Starling, and Jordan Weiss). arXiv

2019

  • December 2019: “Large-scale multiple hypothesis testing with the normal-beta prime prior” (with Malay Ghosh) has been published in Statistics. Paper

2018

  • September 2018: “High-dimensional multivariate posterior consistency under global-local shrinkage priors” (with Malay Ghosh) has been published in Journal of Multivariate Analysis. Paper

  • August 2018: I graduated from the University of Florida with my PhD in Statistics and joined the University of Pennsylvania as a postdoctoral researcher.

  • April 2018: “Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A comparative study” (with Isaac Duerr, Hunter R. Merrill, Chuan Wang, Mackenzie Boyer, Michael D. Dukes, and Nikolay Bliznyuk) has been published in Environmental Modelling & Software. Paper