Gongbo "Tony" Liang, PhD
Assistant Professor of Computer Science
Texas A&M University-San Antonio
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About Me
Hello, I'm Gongbo "Tony" Liang, an IEEE Senior Member, Vice Chair of the IEEE Lone Star EMBS Chapter, and Assistant Professor of Computer Science at Texas A&M University-San Antonio. Since 2014, I have been dedicated to the field of computer vision and multi-modality learning, with a passion for making our artificial intelligence systems more trustworthy and robust. My research addresses foundational challenges in modern AI, where I develop novel algorithms for uncertainty-aware learning, model calibration, and explainability. This work directly strengthens the cybersecurity of AI systems by making them more robust against adversarial attacks, more transparent, and more reliable.
My journey in this field was formalized with a Ph.D. in Computer Science from the University of Kentucky under the supervision of Dr. Nathan Jacobs (moved to Washington University in St. Louis). Today, my work bridges the gap between theory and practice. The goal is to apply these trustworthy AI solutions to critical real-world problems, from improving diagnostic precision in medical imaging to enhancing roadway safety in rural areas and mitigating bias in generative AI. As a teacher and mentor, I am committed to a hands-on, project-based approach, empowering students from diverse backgrounds to excel in AI and contribute to a more equitable technological future.
My research and educational initiatives have been generously supported by funding from organizations like the NSF, NASA, NIH, Google, and University of Kentucky. I am always looking to connect with motivated students who are eager to tackle challenging problems in trustworthy AI. If you're interested in joining my team, please check out the Openings page to learn more.
News
- 11/06/2026: Our paper on satellite-based crash risk estimation, led by my students Ahmad Elallaf and Noah Sizemore, has been accepted to the GeoCV Workshop @ WACV'27. Congratulations to the team!
- "Location-Conditioned Crash Risk Estimation from Satellite Imagery" [arXiv]
- 10/19–20/2026: Grateful to have been selected from more than 350 applicants to participate in the NSF 2026 SaTC Aspiring PIs Workshop at the Johns Hopkins University Bloomberg Center in Washington, DC.
- 09/29/2026: Happy to share that two of our IEEE IROS Workshop papers won the Best Paper Awards
- 09/03/2026: Honored to be selected as Vice Chair of the IEEE Lone Star EMBS Chapter, serving a term from 09/03/2026 to 12/31/2027.
- 06/18/2026: Happy to announce that I have a paper accepted to ECCV'26 (A*, H5:262).
- "Contrastive Conditional–Unconditional Alignment for Long-tailed Diffusion Model" [code]
- 03/31/2026: Congrats to Trevor on his paper being accepted to IEEE JSTARS (FI:5.3, Q1).
- "Recent Advancements in Deep Learning for SAR-to-Optical Image Translation: A Systematic Literature Review (2021-2025)"
- 01/03/2026: Congrats to Ahmad and Yuktha on their papes being accepted to WACV'26 workshop (Accept Rate: 26.7%).
- "MedProbCLIP: Probabilistic Adaptation of Vision–Language Foundation Model for Reliable Radiograph–Report Retrieval"
- 12/16/2026: Congratulations to my former student, Cristian Moran, on being selected as Texas A&M University-San Antonio’s 20,000th graduate honoree and receiving the Outstanding Senior recognition.
- 12/05/2025: Congrats to Ahmad and Yuktha on winning the Best Poster award at the 2nd Annual IEEE LSS EMB Workshop on AI and Healthcare.
- 11/03/2025: Glad to announce that our paper, BetaRisk for probabilistic traffic risk estimation, has been accepted to AAAI'26 (A*, H5: 232, Acceptance Rate: 17.6%).
- 08/22/2025: Received the Excellence in Scholarship Award from the College of Arts and Sciences at Texas A&M University–San Antonio.
- 06/24/2025: Our paper about LLM-generated Python code evaluation is accepted to IEEE Access (IF: 3.4, Q1).
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