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Research Overview
Research Overview
Dr. Wang's research lies at the intersection of statistics, artificial intelligence, and data science, focusing on the development of interpretable, scalable, and trustworthy methodologies for complex and high-dimensional, and heterogeneous data. Her work combines theoretical innovation with computational efficiency to advance the scientific understanding of data arising from longitudinal/functional data, heterogeneous data, time series, spatiotemporal data, and large-scale imaging data.
Her methodological research encompasses nonparametric and semiparametric modeling and inference, uncertainty quantification, statistical learning for complex data structures, functional and spatiotemporal data analysis, statistical imaging, and high-dimensional modeling. Working at the interface of statistics, mathematics, and computer science, she is committed to building the statistical and algorithmic foundations of machine learning and artificial intelligence, particularly in the areas of data science, large-scale analytics, and trustworthy AI.
The methodologies Dr. Wang has developed have been applied to a wide range of domains, including epidemiology, biomedical science, neuroimaging, environmental studies, financial economics, engineering, and the social sciences. Her long-term goal is to create rigorous, transparent, and ethically grounded data science frameworks that enhance reproducibility, fairness, and interpretability in real-world decision-making.
Statistical Learning
- Nonparametric Modeling & Inference
- Data with Complex Features (Functional, longitudinal, spatiotemporal, high-dimension)
- Statistical Imaging
- Uncertainty Quantification
Big Data Analytics
- Distributed Learning
- Federated Learning
Artificial Intelligence
- Generative AI
- Trustworthy AI
Data Science
- Data Visualization
- Data Fusion
Neuroimaging
Epidemiology
Medical Studies
Engineering
Environment
Genetics
Economics
Plant Science
Social Science
National Institutes of Health (NIH 1R01AG090610): Co-Investigator (PI: Alicia Hong, $3M), 9/15/2025-8/31/2030 [Read News]
National Science Foundation DMS-2426173: Principal Investigator ($248,742), 09/01/2024--08/31/2027 [Read News]
National Institutes of Health (NIH 1R01AG085616): Lead Principal Investigator ($1,199,772), 09/01/2023--04/30/2027 [Read News]
National Science Foundation CMMI-2139816: Co-Investigator (PI: Jane Rongerude, $635,420), 11/01/2021--10/31/2024
National Science Foundation DMS-2203207: Sole Principal Investigator ($107,317), 10/01/2021 - 07/31/2023
National Science Foundation (RAPID) 2050264: Co-Investigator (PI: Jane Rongerude, $60,000), 09/01/2020--08/31/2021
National Science Foundation DMS-1916204: Sole Principal Investigator ($124,999), 08/01/2019--07/31/2022
US Securities and Exchange Commission: Sole Principal Investigator ($230,686), 09/10/2019--08/31/2020
National Science Foundation DMS-1542332: Sole Principal Investigator ($99,999), 07/01/2014--07/31/2017
National Science Foundation SES-1357585: Co-Investigator (PI: Robert Belli, $300,000), 08/01/2014--07/31/2017
National Science Foundation DMS-1309800: Sole Principal Investigator ($99,999), 08/15/2013--06/30/2014
National Science Foundation DMS-1106816: Co-Investigator (PI: Xiao Song, $149,966), 08/01/2011--07/31/2014
ASA/NSF/BLS Senior Research Fellowship: Sole Principal Investigator ($77,000), 08/01/2010--08/14/2011
National Science Foundation DMS-0905730: Sole Principal Investigator ($100,200), 07/01/2009--06/30/2013
[Authors marked with * conducted the work during their PhD studies under my supervision.]
Gu. Z.*, Yu, S., Wang, G. and Wang, L. (2025+). Boosting AI-generated biomedical images with confidence through advanced statistical inference. Journal of the American Statistical Association, Theory and Methods. Special Issue on Statistical Science in AI. Forthcoming. [Read PDF]
Kim, M.*, Wang, L. and Wang, H. (2025+). Quantile spatially varying coefficient models. Journal of the American Statistical Association, Theory and Methods. Forthcoming. [Read PDF] [Code]
Yu, S., Wang, G. and Wang, L. (2024). Distributed heterogeneity learning for generalized partially linear models with spatially varying coefficients. Journal of the American Statistical Association, Theory and Methods. [Read PDF]
Li, X., Yu, S., Wang, Y.*, Wang, G., Lai, M. J. and Wang, L. (2024). Nonparametric regression for 3D point cloud learning. Journal of Machine Learning Research, 25(102):1–56. Read PDF
Wang, L., Wang, G., Li, X.*, Yu, S.*, Kim, M.*, Wang, Y.*, Gu, Z.* and Gao, L. (2021). Modeling and forecasting COVID-19. AMS: Notices Of The American Mathematical Society, 68, 585-595. [Read PDF]
Li, X.*, Wang, L. and Wang, H. (2021). Sparse Learning and Structure Identification for Ultra-High-Dimensional Image-on-Scalar Regression. Journal of the American Statistical Association, Theory and Methods, 116, 1994-2008.
Yu, S.*, Wang, G., Wang, L. and Yang, L. (2021). Multivariate spline estimation and inference for image-on-scalar regression. Statistica Sinica, 31, 1463-1487. [Read PDF] [An early version was selected as one of three runners-up of the 2019 ASA Statistics in Imaging Section Student Paper Competition]
Wang, Y.*, Wang, G., Wang, L. and Ogden, T. (2020). Simultaneous confidence corridors for mean functions in functional data analysis of imaging data. Biometrics, 76, 427-437. [Read PDF]
Yu, S.*, Wang, G., Wang, L., Liu, C.* and Yang, L. (2020). Estimation and inference for generalized geoadditive models. Journal of the American Statistical Association, Theory and Methods, 115, 761-774. [Read PDF]
Mu, J.*, Wang, G. and Wang, L. (2018). Estimation and inference in spatially varying coefficient models. Environmetrics, 29:e2485. [Read PDF] (Environmetrics's top 20 most downloaded recent papers!)
Song, X. and Wang, L. (2017). Partially time-varying coefficient proportional hazards models with error prone time-dependent covariates: an application to the AIDS Clinical Trials Group 175 Data. Annals of Applied Statistics, 11, 274-296. [Read PDF]
Wang, L., Xue, L., Qu, A. and Liang, H. (2014). Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates. Annals of Statistics, 42, 592-624. [Read PDF]
Lai, M. J. and Wang, L. (2013). Bivariate penalized splines for regression. Statistica Sinica, 23, 1399-1417. [Read PDF]
Wang, L., Liu X., Liang, H. and Carroll, R. J. (2011). Estimation and variable selection for generalized additive partial linear models. Annals of Statistics, 39, 1827-1851. [Read PDF]
Wang, L. and Yang, L. (2007). Spline-backfitted kernel smoothing of nonlinear additive autoregression model. Annals of Statistics, 35, 2474-2503. [Read PDF]
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