Dr. Gongbo "Tony" Liang earned his Ph.D. in Computer Science degree from the University of Kentucky under the supervision of Dr. Nathan Jacobs. Before joining TAMUSA, Dr. Liang was an Assitant Professor at Eastern Kentucky University (EKU) and the Director of the EKU Gaming Institute.

Dr. Liang has focused on computer vision and deep neural network research since 2014. He is excited about modern deep neural networks to solve previously unsolvable domain-specific challenges while tackling the fundamental issues of deep neural networks. He has projects in several domains, including medical imaging, astrophysics and astronomy, and natural language processing. He is also interested in neural network adversarial attack and defense, as well as adopting neural networks for cybersecurity. Dr. Liang has over 30 peer-reviewed publications and two awarding abstracts. He also has one AI algorithm for breast cancer diagnosis licensed to the industry.  [Google Scholar] [DBLP] [ORCID] [Personal Site] 

Selected Publications

Journal Articles

• Liu, L., Zhang, P., Liang, G., Xiong, S., Wang, J., & Zheng, G. (2022). A Spatiotemporal Correlation Deep Learning Network for Brain Penumbra Disease. Neurocomputing, 520, no.1 (2023):274-283. (Impact Factor:5.779)
• 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)
• 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: 7.021)
• 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)

Conference Papers

• Zhang, Y., Liang, G., & Jacobs, N. (2021, November). Dynamic Feature Alignment for Semi-Supervised Domain Adaptation. The 32nd British Machine Vision Conference (BMVC). Acceptance Rate: 26.19% [434/1657]. H5-Index: 66.
• Liang, G., Zhang, Y., Wang, X., & Jacobs, N. (2020, September). Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification. The 31st British Machine Vision Conference (BMVC). Acceptance Rate: 21.01% [195/928]. H5-Index: 68.
• Liang, G., Li, Q., & Kang, X. (2016, September). Pedestrian Detection via a Leg-driven Physiology Framework. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 2926-2930). IEEE. H5-Index: 60.

Student Papers

• Xing, E.,  Liu, L., Xing, X., Qu, Y., Jacobs, N., & Liang, G. (2022, August). Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity. The 26th International Conference on Pattern Recognition (ICPR). IEEE. H5-Index: 43.
• Qu, Y., Yan, D., Xing, E., Zheng, F., Zhang, J., Liu, L., & Liang, G. (2022, July). Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3849-3853). IEEE. H5-Index: 39.
• Ying, Q., Xing, X., Liu, L., Lin, A. L., Jacobs, N., & Liang, G. (2021, November). Multi-modal Data Analysis for Alzheimer’s Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3586-3591). IEEE. H5-Index: 39.