Gongbo "Tony" Liang, PhD
Assistant Professor of Computer Science
Texas A&M University-San Antonio
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Research

I'm a computer vision researcher dedicated to improving the trustworthiness of neural networks. I'm also excited to push the boundaries of deep learning and apply innovative solutions to real-world challenges. Please visit my publication page to learn more.

Biography

Dr. Gongbo "Tony" Liang earned his Ph.D. in Computer Science degree from the University of Kentucky (2020) under the supervision of Dr. Nathan Jacobs (moved to Washington University in St. Louis). 
Dr. Liang is an Assistant Professor of Computer Science at Texas A&M University-San Antonio. He 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.

Prospective Students

I am always interested in hard-working students with strong technical abilities, a desire to grow, and an eagerness to work on challenging problems as part of a team. If you are interested, please see the openings page.

News

  • 11/01/2024: One paper about medcial imaging model calibration is accepted to IEEE BigData'25  (CORE Ranking: B, H5-index: 54)
  • 06/18/2024: Exciting news! We've been awarded a one-year pilot research grant from Google to explore Image Generation Bias
  • 05/28/2024: A paper about multi-classification of neuropsychiatric disorders is accepted to IEEE Journal of Biomedical and Health Informatics (IF: 7.7, Q1)
  • 04/24/2024: Congrats to Cristian Moran for receiving the First Place Poster Award at the 10th Texas A&M University-San Antonio Student Research Symposium.
    • Title: Spatial-Temporal Roadway Accident Visualization and Analysis in San Antonio
  • 02/01/2024: Congrats to one of my undergraduate students received the Dream.US Scholarship
  • 11/06/2023: A paper about road safety estimation is accepted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IF: 5.5, Q1). 
See more here....