Hairong Wang

https://hairongw.github.io/

Dr. Hairong Wang is an Assistant Professor in the Operations Research & Industrial Engineering program at the University of Texas at Austin. Her research focuses on the development of machine learning models and algorithms for high-dimensional, multi-modal data with complex, heterogeneous structures. In particular, she develops data-driven methodologies for building and training machine learning models with data and computational efficiency, interpretability, generalizability, and robustness, and propose principled approaches to fuse domain knowledge into model design for supporting clinical diagnosis and optimal treatment in high-stake scenarios, such as brain cancer, liver cancer, Alzheimer’s disease. Throughout her academic career, Dr. Wang’s contributions to both research and teaching have been recognized with multiple awards, including the Wally George Fellowship, the David Cowan Scholarship, the George Fellowship, and the Data Mining & Decision Analytics (DMDA) Best Paper Award. She serves as a council member in the INFORMS Data Mining Society, a reviewer for leading journals, and a judge for best paper competitions in Data Analytics & Information Systems (DAIS), Data Mining (DM) and Quality Statistics and Reliability (QSR) Societies. Dr. Wang received her PhD in Operations Research from the School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech, she received her BA in Mathematics from University of Oxford.

Selected Publications

1. Wang, H., Argenziano, M.G., Yoon, H., Boyett, D., Save, A., Petridis, P., Savage, W., Jackson, P., Hawkins-Daarud, A., Tran, N., Hu, L., Singleton, K.W., Paulson, L., Al-Dalahmah, O., Bruce, J.N., Grinband, J., Swanson, K.R., Canoll, P., Li, J. (2024), “Biologically Informed Deep Neural Networks Provide Quantitative Assessment of Intratumoral Heterogeneity in Post-Treatment Glioblastoma.” npj Digital Medicine 7(1), 292.
2. Melo, J.G., Monteiro, R.D.C., Wang, H. (2024), “A Proximal Augmented Lagrangian Method for Linearly Constrained Nonconvex Composite Optimization Problems.” Journal of Optimization Theory and Applications 202(1), 388–420.
3. Wang, H.^*, Mao, L.^* (equally contributed first authors), Zhang, Z., Li, J. (2025), “SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical Knowledge-Informed Label Smoothing.” IISE Transactions on Healthcare Systems Engineering, 1–12.
4. Wang, L., Wang, H., Su, Y., Lure, F., Li, J. (2024), “A Novel Hybrid Ordinal Learning Model with Health Care Application.” IEEE Transactions on Automation Science and Engineering 22, 339–352.
5. Wang, L., Wang, H., D’Angelo, F., Curtin, L., Sereduk, C.P., De Leon, G., Singleton, K.W., Urcuyo, J., Hawkins-Daarud, A., Jackson, P., Krishna, C., Zimmerman, R.S., Patra, D.P., Bendok, B.R., Smith, K.A., Nakaji, P., Donev, K., Baxter, L.C., Mrugała, M.M., Ceccarelli, M., Iavarone, A., Swanson, K.R., Tran, N., Hu, L., Li, J. (2024), “Quantifying Intra-Tumoral Genetic Heterogeneity of Glioblastoma Toward Precision Medicine Using MRI and a Data-Inclusive Machine Learning Algorithm.” PLOS One 19(4), e0301178.
6. Mao, L.^*, Wang, H.^* (equally contributed first authors), Hu, L., Tran, N., Canoll, P., Swanson, K.R., Li, J. (2024), “Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.” IEEE Transactions on Automation Science and Engineering.