Ray Bai
LightNews LightNews
banner
raybai07.bsky.social
Ray Bai
@raybai07.bsky.social
190 followers 200 following 66 posts
Assistant Professor of Statistics at George Mason University, French bulldog owner, and pop culture enthusiast. Any views expressed on here are my own, not my employer's. 👨‍🏫📊 📚👨‍🍳🏳️‍🌈 raybai.net
Posts Replies Media Videos
raybai07.bsky.social
Congrats to my student Leah Wood for successfully defending her Senior honors thesis "Spatiotemporal Modeling of Maternal Mortality in South Carolina 2018-2023"! Leah will pursue a Masters in Biostatistics next.

This was on par with an excellent Masters thesis, tbh. Great job!
November 5, 2025 at 8:06 PM
raybai07.bsky.social
Having a great time visiting Columbia, SC and catching up with old friends and coworkers! I will always be grateful to @uofscstatistics.bsky.social for helping me to launch my career!
November 5, 2025 at 2:54 AM
raybai07.bsky.social
One week till my trip to Columbia, SC to see my Honors student Leah defend her senior thesis! She did an excellent job on Bayesian spatiotemporal modeling of maternal mortality in South Carolina from 2018-2023. She coded up the model in Stan & R and produced some very nice maps!
October 29, 2025 at 12:40 AM
raybai07.bsky.social
Today is my 40th birthday, and I had a very special treat for it -- getting to meet one of my idols, Dr. Jianqin Fan! Dr. Fan's papers on SCAD penalty and sure independence screening for high-dimensional data were among the first papers I read as a PhD student. So inspiring!
September 26, 2025 at 8:15 PM
raybai07.bsky.social
Turning 40 in a week (!). Here are some old photos of me when I was 30-31 yrs old and much more fit/30 pounds lighter. Need to get back into shape, maybe join a local running group again!
September 19, 2025 at 1:16 PM
raybai07.bsky.social
I'm pleased to share that my single-authored paper "Bayesian group regularization in generalized linear models with a continuous spike-and-slab prior" has been accepted for publication in Annals of the Institute of Statistical Mathematics!

Link: raybai.net/wp-content/u...
ABSTRACT: We study Bayesian group-regularized estimation in high-dimensional generalized linear models (GLMs) under a continuous spike-and-slab prior. Our framework covers both canonical and non-canonical link functions and subsumes logistic, Poisson, negative binomial, and Gaussian regression with group sparsity. We obtain the minimax L2 convergence rate for both a maximum a posteriori (MAP) estimator and the full posterior distribution under our prior. Our theoretical results thus justify the use of the posterior mode as a point estimator. The posterior distribution also contracts at the same rate as the MAP estimator, an attractive feature of our approach which is not the case for the group lasso. For computation, we propose expectation-maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms. We illustrate our method through simulations and a real data application on predicting human immunodeficiency virus (HIV) drug resistance from protein sequences.
September 16, 2025 at 12:11 PM
raybai07.bsky.social
Our paper, "Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression," has been accepted for publication in Computational Statistics!

Read it here: raybai.net/wp-content/u...
September 15, 2025 at 5:45 PM
raybai07.bsky.social
University of Florida crew now at George Mason University Statistics Dept!
Ray Bai (PhD student at UF from 2014-2018), Brenda Betancourt (Assistant Professor at UF from 2018-2022), and Abolfazl Safikhani (Assistant Professor at UF from 2019-2022). Small world!
September 5, 2025 at 5:24 PM
raybai07.bsky.social
Had such a great time at our going away get-together tonight! We are so grateful for the friends we made in Columbia, SC. We will be back to visit again. Come visit us in DC! ❤️
August 17, 2025 at 1:47 AM
raybai07.bsky.social
Last day in the office at USC, and my advisees came to meet with me one last time! My Senior Honors thesis student Leah, my Summer REU student Evan, & my PhD students Fanghua and Sijian. So fortunate to have had the opportunity to work with outstanding students at USC. #professor #proudadvisor
August 15, 2025 at 8:44 PM
raybai07.bsky.social
Last week on University of South Carolina's campus. I will be in the office on Friday for some final in-person meetings and for some final orders of business.

If you're at USC on Fri., please come say goodbye!
August 13, 2025 at 6:06 PM
raybai07.bsky.social
New preprint "Parameter expanded variational Bayes for well-calibrated high-dimensional linear regression with spike-and-slab priors"!

Read the full paper here: www.researchgate.net/publication/...
August 1, 2025 at 8:53 PM
raybai07.bsky.social
Today is my last "official" day at University of South Carolina. Next month, I will be moving to the D.C. area and continuing my academic career at George Mason University. However, I really cherish my time as a faculty member at USC. #ForeverToThee

Read my full post here: tinyurl.com/4fpzjeru
Posing with the Cocky the Gamecock statue Standing in front of the LeConte College sign (where the USC Statistics Department is located)
July 31, 2025 at 2:03 PM
raybai07.bsky.social
Hard at work this summer before the Fall semester begins. Another new preprint "BiSSLB: Binary spike-and-slab biclustering for binary datasets" with my PhD student Sijian Fan should be available in the next few weeks!
SHORT ABSTRACT: In this work, we propose a novel Bayesian approach for biclustering binary datasets called Binary Spike-and-Slab Lasso Biclustering (BiSSLB). Our method is based on a logistic matrix factorization model with spike-and-slab lasso priors on the latent spaces. We further incorporate an Indian Buffet Process (IBP) prior to automatically determine the number of biclusters. To avoid the high computational cost of EM algorithms, we propose a novel coordinate ascent algorithm with proximal steps which allows for scalable computation. The performance of our proposed approach is assessed via simulations and two real applications on leukemia gene expression data and protein-protein interaction (PPI) data, where BiSSLB is shown to outperform other state-of-the-art binary biclustering methods.
July 24, 2025 at 2:16 PM
raybai07.bsky.social
Another new preprint coming soon, hopefully in the next few weeks! This is work from the summer REU that I supervised this past summer. The undergrad students did a great job on this!
ABSTRACT: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of hospitalization and death in the United States. Although progress has been made in understanding both patient-level and community-level risk factors associated with COPD, there is little work on measuring the causal effects of community exposures on COPD risk. In particular, it remains unclear whether neighborhood income or neighborhood exposure to vape shops causes the number of emergency department (ED) visits for COPD to increase. In this study, we introduce a Bayesian spatial causal inference model to determine the average causal effect of median income and vape shop density on the number of COPD ED visits. Using county-level data from the state of North Carolina in 2023, we found that an increase in a county's median income caused a significant decrease in that county's COPD ED visits. On the other hand, greater exposure to vape shops did not cause a significant change in the number of COPD ED visits. Our findings enhance understanding of how community exposures impact COPD hospitalizations and may aid in place-based interventions to reduce the number of ED visits for COPD.
July 21, 2025 at 1:59 PM
raybai07.bsky.social
New preprint coming soon! Variational Bayes (VB) is a scalable approach for conducting Bayesian variable selection when p>>n, but the standard mean-field CAVI algorithm results in poorly calibrated predictions. We introduce a novel parameter-expanded sheme to correct this issue.
Title: Parameter-Expanded Variational Bayes for Well-Calibrated High-Dimensional Linear Regression with Spike-and-Slab Priors. Abstract: As scientific problems grow in complexity, there is a pressing need for robust and scalable computational methods for fitting high-dimensional statistical models. Variational Bayes (VB) provides an approximate alternative to traditional sampling-based Bayesian inference, often reducing computation time from days to hours or minutes. VB typically minimizes the Kullback-Leibler divergence via coordinate ascent under a mean-field assumption. Its performance can be highly sensitive to prior specifications, particularly in sparse high-dimensional regression with spike-and-slab priors. A significant limitation of standard VB is its tendency to produce poorly calibrated predictions; that is, the predicted values often exhibit a systematic bias relative to the observed outcomes, failing to accurately reflect the true conditional expectation. Motivated by this, we apply parameter expansion to VB and propose a sparse parameter-expanded VB (spexvb) algorithm that improves robustness to prior settings and enhances predictive calibration. Compared to standard VB, spexvb delivers more stable and accurate posterior estimates. We evaluate its performance through extensive simulations and a real-world application, demonstrating the practical advantages of parameter expansion in variational inference for high-dimensional regression.
July 19, 2025 at 12:23 PM
raybai07.bsky.social
The Summer REU I'm supervising wraps up next week! My students did an excellent job studying spatial models for disease mapping and causal inference, and implementing these models on real public health data from North Carolina! Looking forward to their final report and presentation!
July 3, 2025 at 6:08 PM
raybai07.bsky.social
Celebrating 8 years of #love and #pride! Happy Pride Month! 🧑‍🤝‍🧑🏳️‍🌈
June 30, 2025 at 12:23 PM
raybai07.bsky.social
(1/2) My summer REU students did such a great job! They used county-level data and a Bayesian conditional autoregressive (CAR) model to estimate the North Carolina counties' relative risks for COPD ED visits. Not an easy project for undergrads but they did it!

(cont...)
June 19, 2025 at 11:09 PM
raybai07.bsky.social
If anyone is attending the IISA 2025 Conference at U. Nebraska-Lincoln this week, check out Session 04.M2.I72: Recent advances in high-dimensional modeling on Sun., Jun 15, 10:45 am-12:15 pm! My recent work on Bayesian VAR models (w/ Partha Sarkar of FSU) will be presented then!
Session 04.M2.172: Recent advances in high-dimensional modeling at the IISA 2025 Conference at the University of Nebraska-Lincoln is from 10:45 am-12:15 pm this Sunday, June 15. Abstract of talk "Misspecified Yet Credible: A Generalized Bayes Framework for Uncertainty Quantification in High-Dimensional Bayesian Vector Autoregressive Models": Vector autoregressive (VAR) models are widely used to capture linear dependencies in multivariate time series, yet Bayesian inferential theory for high-dimensional VAR remains largely undeveloped. We propose a generalized Bayes framework that automatically adapts to sparsity and is robust to misspecification of both the error distribution and covariance structure. Under mild regularity conditions, we show that this approach yields reliable uncertainty quantification for the VAR transition matrices in very high dimensions. As a corollary, the same strategy also delivers valid inference for sparse high-dimensional stochastic regressions with serially corelated errors.
June 12, 2025 at 3:08 AM
raybai07.bsky.social
Some other good news! My paper "Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm" (with Anja Zgodic, Jiajia Zhang, Peter Olejua, and
Alexander McLain) has been accepted by Statistics and Computing!

Link to paper: tinyurl.com/yu2f8rrv
SHORT ABSTRACT: High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. To properly account for dependence between longitudinal observations, statistical methods for high-dimensional linear mixed models (LMMs) have been developed. However, few packages implementing these high-dimensional LMMs are available in the statistical software R. Additionally, some packages suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-ConditionalMinimization (ECM) algorithm for computationally efficient maximum a posteriori (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. A real-world example is provided using data from a study of lupus in children with 15,424 genetic and clinical predictors.
May 23, 2025 at 1:23 PM
raybai07.bsky.social
New paper (with Qingyang Liu, Shijie Wang, and Dipankar Bandyopadhyay)! Motivated by periodontal studies, we introduce a new neural network-based robust single-index model. Our method is suitable for skewed and heavy-tailed data.

Read the preprint here: arxiv.org/abs/2505.02153
SHORT ABSTRACT: Periodontal pocket depth is a widely used biomarker for diagnosing risk of periodontal disease. However, pocket depth typically exhibits skewness and heavy-tailedness, and its relationship with clinical risk factors is often nonlinear. Motivated by periodontal studies, this paper develops a robust single-index modal regression framework for analyzing skewed and heavy-tailed data. Our method has the following novel features: (1) a flexible two-piece scale Student-t error distribution that generalizes both normal and two-piece scale normal distributions; (2) a deep neural network with guaranteed monotonicity constraints to estimate the unknown single-index function; and (3) theoretical guarantees, including model identifiability and a universal approximation theorem.
May 7, 2025 at 12:44 AM
raybai07.bsky.social
New preprint coming out later this month! I'm excited for this work, as I have been continuing to explore deep learning for challenging statistical problems - both from methodological/theoretical and applied perspectives.
Short abstract for "A Montonic Single-Index Model for Skewed and Heavy-Tailed Data: A Deep Neural Network Approach Applied to Periodontal Studies": Periodontal pocket depth is a widely used biomarker for diagnosing risk of periodontal disease. However, pocket depth typically exhibits skewness and heavy-tailedness, and its relationship with clinical risk factors is often nonlinear. Motivated by periodontal studies, this paper develops a robust single-index modal regression framework for analyzing skewed and heavy-tailed data. Our method has the following novel features: (1) a deep neural network (DNN) with guaranteed monotonicity constraints to estimate the unknown single-index function; (2) a flexible two-piece scale Student-t error distribution that generalizes both normal and two piece scale normal distributions; and (3) theoretical guarantees, including model identifiability and a universal approximation theorem.
April 14, 2025 at 3:01 PM
raybai07.bsky.social
I had a great time participating in this virtual panel yesterday! We had over 60 people in attendance from the U.S., Canada, and U.K.! Always happy to help out (aspiring) PhD students and junior faculty in any way that I can.
Zoom screenshot for the virtual panel "Navigating the Academic Job Market for (Bio)Statistics Faculty & Postdoc Positions"
April 2, 2025 at 2:52 PM
raybai07.bsky.social
Looking forward to taking part in this panel discussion on Tue, April 1, 3:30-4:30 PM about the academic job search. Everyone is welcome to join on Zoom! Add this to your calendar if you're interested!

Zoom Meeting: lnkd.in/evxiJbPq
ID: 943 5676 5580
Passcode: 362974
Panel Discussion: Academic Job Search Date: Tuesday, April 1, 2025 ⏰ Time: 3:30-4:30 PM EST 📍 Location: Zoom Meeting: https://lnkd.in/evxiJbPq ID: 943 5676 5580 Passcode: 362974
March 16, 2025 at 1:25 PM