Assistant Professor, Department of Computer Science, University of Texas Rio Grande Valley
Office: EIEAB, Room 3.215, 1201 W University Dr, Edinburg, TX, US, 78539
Email: haoteng.tang@utrgv.edu
Google Scholar: Google Scholar
University Webpage: Official Page
I am a tenure-track Assistant Professor of Computer Science at the University of Texas Rio Grande Valley. I received my Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh in 2023, where I was advised by Dr. Liang Zhan. My research focuses on medical image computing, interpretable and robust artificial intelligence, graph learning, and neuroimaging-genetic analysis of brain diseases.
We are dedicated to advancing the study of brain diseases through the development of cutting-edge AI algorithms that integrate large-scale neuroimaging data, multimodal brain connectomics, and genetic profiles. By bridging neuroimaging and imaging-genetics with artificial intelligence, my research aims to enable early diagnosis, identify novel biomarkers, uncover genotype–phenotype associations, and explore the complex relationships between brain disorders and clinical phenotypes. This integrative, data-driven approach supports the goals of precision medicine and deepens our understanding of neurological and psychiatric conditions at both the molecular and systems levels.
Support: 1. UTRGV Presidential Research Fellowship(2023-2025); 2. The UTRGV Faculty Seed Grant(2023-2024)
Principal investigator: Haoteng Tang
We develop novel AI algorithms—including deep neural networks and large foundation models—to address advanced challenges in medical image analysis across various organs. Our work focuses on tasks such as image segmentation, image quality enhancement, multimodal image fusion, and the estimation of clinical measures from imaging data.
Support: National Science Foundation:Computer and Information Science and Engineering Research Expansion Program (NSF-MSI, 2025-2028)
Principal investigator: Haoteng Tang
We are committed to developing interpretable and trustworthy AI models that support reliable decision-making in medical imaging. Our work focuses on integrating model transparency, uncertainty quantification, and clinically meaningful explanations into the design of deep learning and foundation model architectures. Our goal is to ensure that AI-driven insights are not only accurate but also interpretable and actionable for clinicians.
Support: UTRGV Presidential Research Fellowship(2023-2025)
Principal investigator: Haoteng Tang
Huang, Qi, Haoteng Tang, Keyan Wang, Ran Li, Cihat Eldeniz, Natalie Nguyen, Thomas H. Schindler et al. "Model‐based self‐supervised learning for quantitative assessment of myocardial oxygen extraction fraction and myocardial blood volume." Magnetic resonance in medicine (2025).
Zhan, Marcus, Kun Zhao, Guodong Liu, and Haoteng Tang*. "A General Paradigm for Fine-Tuning Large Language Models in Alzheimer’s Disease Diagnosis." In Proceedings of the AAAI Symposium Series, vol. 5, no. 1, pp. 37-42. 2025.
Haoteng Tang*, Guodong Liu, Siyuan Dai, Kai Ye, Kun Zhao, Wenlu Wang, Carl Yang et al. "Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 227-237. Cham: Springer Nature Switzerland, 2024.
Haoteng Tang*, Siyuan Dai, Eric M. Zou, Guodong Liu, Ryan Ahearn, Ryan Krafty, Michel Modo, and Liang Zhan. "Ex-vivo hippocampus segmentation using diffusion-weighted mri." Mathematics 12, no. 7 (2024): 940.
Kai, Ye, Haoteng Tang*, Siyuan Dai, Lei Guo, Johnny Yuehan Liu, Yalin Wang, Alex Leow, Paul M. Thompson, Heng Huang, and Liang Zhan. "Bidirectional mapping with contrastive learning on multimodal neuroimaging data." In International conference on medical image computing and computer-assisted intervention, pp. 138-148. Cham: Springer Nature Switzerland, 2023.
Haozhe Jia, Haoteng Tang, Guixiang Ma, Weidong Cai, Heng Huang, Liang Zhan, and Yong Xia. "A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images." Computers in biology and medicine 155 (2023): 106698.
Haoteng Tang, Lei Guo, Xiyao Fu, Yalin Wang, Scott Mackin, Olusola Ajilore, Alex D. Leow, Paul M. Thompson, Heng Huang, and Liang Zhan. "Signed graph representation learning for functional-to-structural brain network mapping." Medical image analysis 83 (2023): 102674.
Haoteng Tang, Guixiang Ma, Lei Guo, Xiyao Fu, Heng Huang, and Liang Zhan. "Contrastive brain network learning via hierarchical signed graph pooling model." IEEE transactions on neural networks and learning systems (2022).
Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, and Liang Zhan. "Commpool: An interpretable graph pooling framework for hierarchical graph representation learning." Neural Networks 143 (2021): 669-677.
Chao Li, Haoteng Tang, Cheng Deng, Liang Zhan, and Wei Liu. "Vulnerability vs. reliability: Disentangled adversarial examples for cross-modal learning." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 421-429. 2020.
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