Gongbo "Tony" Liang

Gongbo "Tony" Liang

Assistant Professor
College Of Business
Department of Computing and Cyber Security

210-784-2373   |gliang@tamusa.edu


Dr. Gongbo "Tony" Liang is an Assistant Professor of Computer Science in the Department of Computing and Cyber Security (CCS) at Texas A&M University-San Antonio (TAMUSA). Before joining TAMUSA, he served as an Assistant Professor of Computer Science and the Director of EKU Gaming Institute at Eastern Kentucky University. Liang is excited about using modern neural networks and machine learning techniques to solve domain-specific problems while tackling some fundamental issues of deep neural networks. He has focused on a variety of problems, including supervised/weakly-supervised learning, medical/astronomical imaging understanding, multimodal integration, and neural network calibration.


  • Liang, G., Ganesh, H., Steffe, D., Liu, L., Jacobs, N., & Zhang, J. (2022). Development of CNN models for the enteral feeding tube positioning assessment on a small-scale data set. BMC Medical Imaging, 22(1), 1-9. (Impact Factor: 1.930)
  • Liu, L., Chang, J., Wang, Y., Liang, G., Wang, Y. P., & Zhang, H. (2022). Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders. Frontiers in Neuroscience, 16. (Impact Factor: 4.870)
  • Lin, S. C., Su, Y., Liang, G., Zhang, Y., Jacobs, N., & Zhang, Y. (2022). Estimating cluster masses from SDSS multi-band images with transfer learning. Monthly Notices of the Royal Astronomical Society, 512(3), 3885-3894. (Impact Factor: 5.235)
  • Liu, L., Chang, J., Wang, Y., Zhang, P., Liang, G., & Zhang, H. (2022). LLRHNet: Multiple lesions segmentation using local-long rang features. Frontiers in Neuroinformatics, 26. (Impact Factor: 3.739)
  • Liang, G., Greenwell, C., Zhang, Y., Xing, X., Wang, X., Kavuluru, R., & Jacobs, N. (2021). Contrastive cross-modal pre-training: A general strategy for small sample medical imaging. IEEE Journal of Biomedical and Health Informatics, 26(4), 1640-1649. (Impact Factor: 5.772)
  • Su, Y., Zhang, Y., Liang, G., ZuHone, J. A., Barnes, D. J., Jacobs, N. B., ... & Jones, C. (2020). A deep learning view of the census of galaxy clusters in illustrating. Monthly Notices of the Royal Astronomical Society, 498(4), 5620-5628. (Impact Factor: 5.235)
  • Hammond, T. C., Xing, X., Wang, C., Ma, D., Nho, K., Crane, P. K., Liang, G., ... & Lin, A. L. (2020). β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline. Communications biology, 3(1), 1-13. (Impact Factor: 6.268)
  • Wang, X., Liang, G., Zhang, Y., Blanton, H., Bessinger, Z., & Jacobs, N. (2020). Inconsistent performance of deep learning models on mammogram classification. Journal of the American College of Radiology, 17(6), 796-803. (Impact Factor: 5.532)
  • Mihail, R. P., Liang, G., & Jacobs, N. (2019). Automatic hand skeletal shape estimation from radiographs. IEEE transactions on nanobioscience, 18(3), 296-305. (Impact Factor: 3.206)
  • Liang, G., Zhang, J., Brooks, M. A., Howard, J., & Chen, J. (2017). Radiomic features of lung cancer and their dependency on CT image acquisition parameters. Medical Physics, 44, 3024. (Impact Factor: 4.506)


  • 2022   Third Place Best Abstract, ACM Mid-Southeast (ACM-MidSE) Conference
  • 2020   Outstanding Ph.D. Student in Computer Science, University of Kentucky
  • 2019   Second Place Best Poster, Markey Cancer Center Research Day



Courses Teaching

Subject Number Section Description Term Syllabus
CSCI 1436 002 Programming Fundamentals I Fall 2022

No Syllabi Attached

CSCI 3304 001 Database Systems Fall 2022

No Syllabi Attached

CSCI 1436 02L Programming Fundamentals I Lab Fall 2022

No Syllabi Attached