Research Areas
Research Areas
Bayesian methods for inference, machine learning uncertainty, survival and longitudinal models, nonlinear state-space models, spatial models, high-dimensional inference.
Areas of Application: electronic health records, epidemiological modeling, ecological models and forecasting.
(* denotes current or former advisee)
Ye, S*, Rakshe, S and Liang, Y. (2025) "High-dimensional statistical inference and variable selection using sufficient dimension association." Revised for Journal of the American Statistical Association.
Alhallaf, A, Vilcaez, J and Liang, Y. (2025) "Investigating the impact of meteorological factors on sea-level variability in the northwestern Arabian gulf: a case study using deep learning and statistical methods for enhanced forecasting." Ocean Modelling. Minor revision.
Liang, Y, Wang, R*, Wang, Y and Liu, T. (2024) "Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels." Intelligence-Based Medicine. Vol 10, 100154.
Chen, X, Liang, Y and Feng, X. (2024) "Influence of model complexity, training collinearity, collinearity shift, predictor novelty, and their interactions on ecological forecasting." Global Ecology and Biogeography. Vol 33, No. 3, 371-384.
Wang, R*, Liang, Y, Miao, Z and Liu, T. (2023) "Bayesian analysis for imbalanced positive-unlabelled diagnosis codes in electronic health records". Annals of Applied Statistics. Vol 17, No.2, 1220-1238.
Poudel, P, Bello, N, Marburger, D, Carver, B, Liang, Y and Alderman, P. (2022). "Ecophysiological modeling of yield and yield components in winter wheat using hierarchical Bayesian analysis". Crop Science. Vol. 62-1, 358-373.
Habiger, J.D. and Liang, Y. (2022) "Policies for replicable research and the community-wide false discovery rate". The American Statistician. Vol. 76, No. 2, 131-141.
Wang, R*, Miao, Z, Liu, T, Liu, M, Grdinovac, K, Song, X, Liang, Y, Delen, D and Paiva, W. (2021) "Derivation and validation of essential predictors and risk index for early detection of diabetic retinopathy using electronic health records". Journal of Clinical Medicine. 10(7).
Liu, X and Liang, Y. (2021) "What it means to respect individuality". Philosophical Studies. 178:2579-2598.
Ye, S*, Liang, Y and Zhang, B. (2020) Bayesian functional mixed-effects models with grouped smoothness for analyzing time-course gene expression data. Current Bioinformatics. 15:1.
Feng, X, Liang, Y, Gallardo, B and Papes, M. (2019) Physiology in ecological niche modeling: using zebra mussel's upper thermal tolerance to refine model predictions through Bayesian analysis. Ecography. Vol. 43-2, 270-282.
Feng, X, Park, DS, Liang, Y, Pandey, R, Papes, M. (2019) Collinearity in ecological niche modeling: confusions and challenges. Ecology and Evolution. Vol. 9, 10365-10376.
Liang, Y. (2019) Graph-based multivariate conditional autoregressive models. Statistical Theory and Related Fields. Vol. 3, 158-169.
Ye, S*, Liang, Y and Ahmad, IA. (2019) Orthogonal series density estimation for complex surveys. Journal of Nonparametric Statistics. Vol. 31, 469-481.
Liang, Y, Li, Y and Zhang, B. (2018) Bayesian nonparametric inference for panel count data with informative observation process. Biometrical Journal. Vol. 60, 583–596.
Liang, Y, Habiger, J.D. and Min, X. (2017) The influence of misspecified covariance on false discovery control when using posterior probabilities. Statistical Theory and Related Fields. Vol. 1, 205-215.
Hendershot, M and Liang, Y. (2017) Estimating judicial accomplishment: Applying the legislative accomplishment strategy to the decisions of the Supreme Court. The Justice System Journal. Vol. 38, 256-276.
Liang, Y and Sun, D. (2016) Identifiability of masking probabilities in the competing risks model with emphasis on Weibull models. Communications in Statistics - Theory and Methods. V. 45, 2143-2157.
Dale, J, Zou, C, Andrews, W, Long, J, Liang, Y and Qiao, L. (2015) Climate, Water use, and land surface transformation in an irrigation intensive watershed - Streamflow responses from 1950 to 2010. Agriculture Water Management. Vol. 160, 144-152.
Liang, Y, Sun, D, He, C and Schootman, M. (2014) Modeling bounded outcome scores using the binomial-logit-normal distribution. Chilean Journal of Statistics. Vol. 5, 3-14.
Liang, Y and Sun, D. (2012) Objective priors for generative star-shape models. Statistics & Probability Letters. Vol. 82, 991-997.
USDA 040901870: Biorepository for Prarie One Solutions: Deveoping and Integrating Pathogen Monitoring Platform. 9/1/2023-8/31/2024. $155K. Co-PI. (subaward PI: T. Hu)
Pandemic Research Planning Grant: 2022-2023. OSU College of Arts & Sciences. $20K. Co-PI.
NSF/NIH R01EY033861: Harnessing Tensor Information to Improve EHR Data Quality for Accurate Data-driven Screening of Diabetic Retinopathy with Routine Lab Results. 9/30/2021-8/31/2025. $1.2M. Co-PI. (PI: T. Liu)
OCAST HR20-017: Role of altered auditory feedback and principles of motor learning in improving speech intelligibility in people with Pakinson's disease. 10/1/2020-9/30/2023. $90K. Co-PI. (PI: R. Kaipa)
OCAST HR18-087: Validating a clinical decision support algorithm developed with big data to diagnose, state, prevent, and monitor a patient's diabetic retinopathy. 8/1/2018-7/31/2020. $90K. Co-PI. (PI: T. Liu)
NSF ACI1531128: Acquisition of shared high performance compute cluster for multidisciplinary computational and data-intensive research. 10/1/2015-9/30/2018. $950K. Senior personnel. (PI: D. Brunson)
NSF OIA1301789: Adapting socio-ecological systems to increased climate variability. 6/1/2013-5/31/2018. $20M. Senior personnel. (PI: R. Huhnke)
Arts & Sciences Summer Research Grant: 2018. $10K.
Dean's Incentive Grant: 2014. $3K.
Dean's Incentive Grant: 2013. $3K.