Xiaofeng Wang Research Program

Research

Overview

The Clinical Machine Learning Lab is interested in developing novel machine learning and artificial intelligence methods and using these techniques to advance health care. Our current on-going research projects include:

• Causal Machine Learning with Real‐World Data

Our research involves developing novel machine learning methodologies to advance causal inference in clinical research. We focus on developing algorithms for causal clustering and refining models to accurately predict heterogeneous treatment effects. Through these advancements, we develop robust modeling frameworks with target trial emulations that effectively leverage real-world evidence (RWE). Our methodologies drive innovations across COVID-19, critical care, and cardiovascular and lung diseases, ultimately improving patient outcomes and informing clinical decision-making.

• Radiomics

Radiomics involves extracting high-dimensional quantitative features from medical images, which are then analyzed using machine learning algorithms to identify patterns and make clinical predictions. We develop various radiomics models aimed at enhancing their quality and clinical utility across multiple medical fields.

• Modeling with MR Fingerprinting in Cardiovascular and Brain Research 

We use the novel Magnetic resonance fingerprinting (MRF) technique to generate high-resolution quantitative tissue property maps (T1 and T2 maps), and develop machine learning and artificial intelligence prediction models in areas of Cardiovascular and Brain Research.

Announcements

30 June, 2023

The Department of Quantitative Health Sciences (QHS) is pleased to announce that Xiaofeng Wang, PhD, has been elected as a 2023 Fellow of the American Statistical Association (ASA).  

The ASA is the world’s largest community of statisticians and is the second-oldest continuously operating professional association in the country. The designation of ASA Fellow has been a significant honor for over 100 years. Under ASA bylaws, only one-third of one percent of the total association membership may be elected as fellows each year. Fellows are evaluated on their contributions to the advancement of statistical science, the impact of their published works, their position with their employer and activities with the ASA and other professional societies. “To be honored, nominees must have an established reputation in the profession and have made outstanding contributions to statistical science,” the ASA says. 

Dr. Wang's citation by the Committee on Fellows reads, “for outstanding scholarly contributions to the methods and applications in the areas of quantitative imaging analysis, clinical data modeling, and approximate Bayesian inference; for significant contributions to collaborative research in medicine”. 

Selected Publications

Peer Reviewed Books

Wang, X.F., Yue, Y.R., and Faraway, J. (2018). Bayesian Regression Modeling with INLA. Chapman & Hall/CRC Computer Science and Data Analysis Series.

Peer Reviewed Article

(over 200 peer-reviewed papers)

Gao, P., Zou, Y., Duggal, A., Huang, S., Liang, F., & Wang, X.F. (2027). Deep survival analysis for competing risk modeling with functional covariates and missing data imputation. Neural Networks, 205, 109429. DOI:  https://doi.org/10.1016/j.neunet.2026.109429.

Wang, X.F., Zhao, W., Wang, Y., Kwon, D. H., Su, T. Y., Obuchowski, N. A., Griswold, M. A., Wang, Z.I., & Ma, D. (2026). Enhancing Study Design and Analysis of MR Imaging Markers Through Measurement Error Modeling. Journal of Magnetic Resonance Imaging, 63(6), 1596-1609.

Wang, X.F., Huang, S., Xu, Y., Zou, Y., Zhang, P., Xie, Y., & Tsuang, W.M. (2026). Multimorbidity patterns and phenotype transitions in patients with clinician-coded long COVID: a multicenter US electronic health record cohort study. BMC Infectious Diseases. 26, 1687.  DOI: https://doi.org/10.1186/s12879-026-13897-0.

Wang, X.F., Albahra, S., Liang, F., Duggal, A., & Dweik, R.A. (2026). Conformal prediction in clinical artificial intelligence: enhancing model reliability and interpretability. Chest, 169(2), 332-335.

Calabrese, C., Wang, Y., Duggal, A., Huang, S., Fan, Y., Kethireddy, S., Sacha, G.L., Calabrese, L. and Wang, X.F. (2025). Effectiveness of nirmatrelvir/ritonavir or molnupiravir use in immunocompromised patients on B-cell–depleting therapy with COVID-19: a target trial emulation study. Clinical Infectious Diseases, 81(6), 1065-1073.

Wang, X.F., Zou, Y., Wang, Y., & Zhang, C. (2025). Estimating heterogeneous treatment effects with target trial emulation: a checklist of causal machine learning for observational data. Chest, 168(4):856–859.

Lin, D.-Y., Huang, S., Milinovich, A., Duggal, A., Wang, X.F. (2024). Effectiveness of XBB.1.5 vaccines and antiviral drugs against severe outcomes of omicron infection in the USA. The Lancet Infectious Diseases, DOI: https://doi.org/10.1016/S1473-3099(24)00150-6.

Lin, D.-Y., Abi Fadel, F., Huang, S., Milinovich, A.T., Sacha, G.L., Bartley, P., Duggal, A. and Wang, X.F. (2023). Nirmatrelvir or Molnupiravir Use and Severe Outcomes From Omicron Infections. JAMA Network Open, 6(9), e2335077.

Wang, X.F., Pennello, G., DeSouza, N.M., Huang, E.P., Buckler, A.J., Barnhart, H.X., Delfino, J.G., Raunig, D.L., Wang, L. , Guimaraes, A.R., Hall, T.J., Obuchowski, N.A. (2023). Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Academic Radiology, 30(2): 215-229.

Wang, X.F., Zein, J., Ji, X. and Lin, D.-Y. (2023). Impact of Vaccination, Prior Infection and Therapy on Omicron Infection and Mortality. Journal of Infectious Diseases, doi: 10.1093/infdis/jiac460.

Ren, R., Fang, K., Zhang, Q., and Wang. X.F. (2023) Multivariate Functional Data Clustering Using Adaptive Density Peak Detection. Statistics in Medicine, 1-18, doi:10.1002/sim.9687.

Sun, Y., Li, J., Xu, Y., Zhang, T., & Wang, X. (2023). Deep learning versus conventional methods for missing data imputation: A review and comparative study. Expert Systems with Applications, 120201.

Wang, Y., Yan, G., Wang, X.F., Li, S., Peng, L., Tudorascu, D.L., and Zhang, T. (2023). A Variational Bayesian Approach to Identifying Whole-Brain Directional Networks with fMRI Data. The Annals of Applied Statistics. 17(1), 518-538.

Sun, H. and Wang, X.F. (2022). High‐dimensional feature selection in competing risks modeling: A stable approach using a split‐and‐merge ensemble algorithm. Biometrical Journal. doi.org/10.1002/bimj.202100164.

Yue, Y., Bolin, D., Rue, H., and Wang, X.F. (2019). Bayesian Generalized Two-way ANOVA Modeling for Functional Data Using INLA. Statistica Sinica, 29(2), 741-767.

Wang, X.F. and Xu, Y. (2017) Fast clustering using adaptive density peak detection. Statistical Methods in Medical Research, 26(6), 2800-2811.

Yue, Y.R. and Wang, X.F. (2016). Bayesian inference for generalized linear mixed models with multiple predictors subject to detection limits. Statistics in Medicine, 35(10):1689-705.

Faraway, J., Mahabal, A., Sun, J., Wang, X.F., Wang, Y., Zhang, L. (2016) Modeling light curves for improved classification of astronomical objects. Statistical Analysis and Data Mining. 9(1), 1-11.

Wang, X.F., and Ye, D. (2015). Conditional density estimation in measurement error problems. Journal of Multivariate Analysis, 133:38-50.

Sattar, A., Sinha, S.K., Wang, X.F. and Li, Y. (2015). Frailty models for pneumonia to death with a left-censored covariate, Statistics in Medicine. 34(14):2266-2280.

Huang E.P., Wang X.F., Roy Choudhury K, McShane L.M., Gönen M., Ye J., Buckler A.J., Kinahan P.E., Reeves A.P., Jackson E.F., Guimaraes A.R., Zahlmann G. (2015). Meta-analysis of the technical performance of an imaging procedure: Guidelines and statistical methodology. Statistical Methods in Medical Research. 24(1):141-74.

Mazzone, P., Wang, X.F., Xu, Y., Mekhail, T., Beukemann, M.C. Na, J., Kemling, J.W., Suslick, K.S., Sasidhar, M. (2012). Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer. Journal of Thoracic Oncology, 7(1): 137-142.

Wang, X.F., Jiang, Z., Daly, J.J. and Yue, G.H. (2012). A generalized regression model for region of interest analysis of fMRI data. NeuroImage, 59(1), 502-510.

Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.