Methodology
Precision Medicine
Liang, M.*, Ning, Y, Smith, M., and Zhao, Y.Q. Inference with non-differentiable surrogate loss in a general high-dimensional classification framework. Minor revision.
Liang, M.*, Ye, T., Zhao, Y.Q. A General Framework for Incorporating Identification Uncertainty in Individualized Treatment Rules. R&R in JRSSB.
Liang, M., Zhao, Y.Q., Lin, D., Cooperberg, M., and Zheng, Y.* (2025). Estimating optimally tailored active surveillance strategy under interval censoring. Biometrics.
Liang, M., and Yu, M. (2023). Relative contrast estimation and inference for treatment recommendation. Biometrics, 4(79), 2920-2932.
Liang, M., Choi, Y.G., Ning, Y, Smith, M., and Zhao, Y.Q. (2022). Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score. Journal of Machine Learning Research, 23(262), 1-65.
Liang, M., and Yu, M. (2022). A semiparametric approach to model effect modification. Journal of the American Statistical Association, 117(538), 752-764.
Liang, M.*, Ye, T., Fu H. (2018). Estimating individualized optimal combination therapies through outcome-weighted deep learning algorithms. Statistics in Medicine, 37(27), 3869-3886.
Tranfer Learning
Liang, M.*, Wu, R., Yang. S, Guo, Y., Zhao, Y.Q. Classification under Outcome Misclassification: Reliability Quantification and Partial Identification. Under review.
Bhattacharya, S.*, Chen, Y., Liang, M.* (Alphabet-order) Multi-task Learning for Semiparametric Models: Late Fusion and Nuisance Estimation. Major Revision in JASA.
Park, J. (student), Liang, M.*, Zhao, Y. Q., and Zhong, X. (2025). Efficient surrogate-assisted inference for patient-reported outcome measures with complex missing mechanism. Electronic Journal of Statistics, 19(1), 1-53.
Liang, M.*, Park, J. (student), Lu, Q., and Zhong, X. (2024). Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes. Biometrics, 80(4).
Liang, M., and Zhao, Y.Q. (2021). Comment on “More efficient policy learning via optimal retargeting” and “Learning optimal distributionally robust individualized treatment rules”. Journal of the American Statistical Association, 116 (534), 690-693.
Collaborative Research in AI
Jiang, H., Imran, M., Zhang, T., Zhou, Y., Liang, M., Gong, K. and Shao, W. (2025). Fast-DDPM: Fast denoising diffusion probabilistic models for medical image-to-image generation. IEEE Journal of Biomedical and Health Informatics.
Balch, J., Ruppert, M., Guan, Z., Buchanan, T., Abbott, K. Shicel, B., Bihorac, A., Liang, M., Tignanelli, C., Loftus, T. (2024). Risk-Specific Training Cohorts Improve Performance of a Deep Learning Surgical Risk Prediction Model. JAMA Surgery, Dec 1;159(12):1424-1431.
Jiang, H., Imran, M., Muralidharan, P., Patel, A., Pensa, J., Liang, M., Benidir, T., Grajo, J.R., Joseph, J.P., Terry, R., and DiBianco, J.M. (2024). MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images. Computerized Medical Imaging and Graphics, 112.
Liang, M., Li, Z., Chen, T., and Zeng, J. (2014). Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(4), 928-937.
Book Chapter
Liang, M., and Zhao, Y.Q. Estimation and inference of individualized treatment rules using efficient augmentation and relaxation learning. Precision Medicines: Methods and Applications, Springer.
* indicates the corresponding author