Dr. Gongbo "Tony" Liang earned his Ph.D. in Computer Science from the University of Kentucky under the supervision of Dr. Nathan Jacobs (moved to Washington University in St. Louis). Before joining TAMU-SA, Dr. Liang was an Assistant 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; his specialty is developing learning-based algorithms and systems for processing large-scale image collections. His research has been funded by NSF, NASA, and Google.  [Google Scholar] [DBLP] [ORCID] [Personal Site] 

Selected Publications

Selected Journal Articles

• Deanda, D., Alsmadi, I., Guerrero, J., and Liang, G. (2025). "Defending Mutation-Based Adversarial Text Perturbation: A Black-box Approach." Cluster Computing, 28, 196. Impact Factor: 5.5. SJR: Q1. DOI: 10.1007/s10586-024-04916-3
• Liang, G,  Zulu, J, Xing, X, & Jacobs, N. (2024). Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation through MultiScale Aerial Images and Self-Supervised Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17 (2024):535-546. Impact Factor: 5.5. DOI: 10.1109/JSTARS.2023.3331438.
• Liu, L., Chang, J., Liang, G., & Xiong, S. (2023). "Simulated Quantum Mechanics-Based Joint Learning Network for Stroke Lesion Segmentation and TICI Grading." IEEE Journal of Biomedical and Health Informatics, 27(7), 3372-3383. Impact Factor: 7.7. DOI: 10.1109/JBHI.2023.3270861.
• Liang, G., Greenwell, C., Zhang, Y., Xing, X., Wang, X., Kavuluru, R., & Jacobs, N. (2022). 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: 7.7. DOI: 10.1109/jbhi.2021.3110805.
• 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. DOI: 10.1093/mnras/stac725.
• 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: 6.3. DOI: 10.1016/j.jacr.2020.01.006. 

Selected Conference Proceedings

• Elallaf, A., Jacobs, N., Ye, X., Chen, M., & Liang, G. (2026, January). "Beta Distribution Learning for Reliable Roadway Crash Risk Assessment." In The 40th Annual AAAI Conference on Artificial Intelligence (AAAI). Singapore. CORE Ranking: A*, H5-index: 232. DOI: Available Soon.
• Xing, G., Salem, T, & Liang, G. (2025, February). "ChartCode: A Flowchart-Based Tool for Introductory Programming Courses." In The 56th ACM Technical Symposium on Computer Science Education (SIGCSE). Pittsburgh, PA, USA. CORE Ranking: A. DOI: 10.1145/3641555.3705140
• Han, B., Masupalli, Y., Xing, X., & Liang, G. (2024, December). "Improving Medical Imaging Model Calibration through Probabilistic Embedding." In IEEE International Conference on Big Data (BigData). Washington, DC, USA. CORE Ranking: B, H5-index: 54. DOI: 10.1109/BigData62323.2024.10825661
• Xing, E.,  Liu, L., Xing, X., Qu, Y., Jacobs, N., & Liang, G. (2022, August). Neural Network Decision-Making Criteria Consistency Analysis via Input Sensitivity. The 26th International Conference on Pattern Recognition (ICPR). IEEE. CORE Ranking: B, H5-Index: 68
• 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]. CORE Ranking: A, H5-Index: 77. LINK: BMVC-2021/1427.
• 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]. CORE Ranking: A, H5-Index: 77. Link: BMVC-2020/0059.