Dr. Gongbo "Tony" Liang earned his Ph.D. in Computer Science degree 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 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; 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

Journal & Conference Papers

• 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.
• 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.
• 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: 77. LINK: BMVC-2021/1427.
• 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. 
• 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: 77. Link: BMVC-2020/0059.

Student Papers

• Deanda, D.,  Alsmadi, I., Guerrero, J., and Liang, G. (2024)  "Defending Mutation-Based Adversarial Text Perturbation: A Black-box Approach." Cluster Computing. Early Access. (IF=3.6, SJR=Q1)
• Han, B., Masupalli, Y., & Liang, G. (2024, December). "Multi-Scale Probabilistic Embedding for Vision Model Calibration." In IEEE International Conference on Big Data (BigData). Washington DC, USA. (CORE Ranking: B, H5-index: 54) 
• Han, B., Moran, C., Yang, J., Lee, Y., Cao, Z., and Liang, G. (2024, July). Multi-Scale Self-Supervised Consistency Training for Trustworthy Medical Imaging Classification. The 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Podium (acceptance rate approx. 28%). H5-Index: 48.
• Liang, G., Guerrero, J., Zheng, F., & Alsmadi, I. (2023). Enhancing Neural Text Detector Robustness with μAttacking and RR-Training. Electronics, 12(8), 1948. Impact Factor: 2.9.
• 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: 56. (CORE Ranking: B)