Contact
Mobile
(434) 243-4449
Location
Rice Hall 509 85 Engineer's Way

Aidong Zhang

Thomas M. Linville Professor Computer Science, Data Science, and Biomedical Engineering
Contact
Mobile
(434) 243-4449
Location
Rice Hall 509 85 Engineer's Way
Website Zhang's Research Group

About

Aidong Zhang's research focuses on developing machine learning approaches to interpretable and fair learning, concept-based learning, AI for Sciences, generative AI and medical applications, such as diagnosis and monitoring of Alzheimer's Disease (AD) and AD-related dementias (ADRD). In particular, she works on AI for Sciences on large language models for hypothesis generations for scientific discovery. Dr. Zhang is Thomas M. Linville Professor of Computer Science, with a joint appointment in the School of Data Science and a courtesy appointment in the Department of Biomedical Engineering at University of Virginia. Her research interests focus on machine learning, AI for sciences, bioinformatics and health informatics.

Note: My lab has position available for creative PhD students, motivated for doing research in the areas of machine learning, AI for Sciences, bioinformatics, and health informatics. If you are interested, please contact me.

Research Interests

Machine Learning
AI for Sciences
Bioinformatics
Health Informatics

Selected Publications

Capturing In-Context Learning Dynamics with Task Operators,
NeurIPS 2026 Guangzhi Xiong, Zhenghao He, Bohan Liu, Sanchit Sinha, Wenqian Ye, Aidong Zhang
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models,
NeurIPS 2026 Guangzhi Xiong, Qiao Jin, Sanchit Sinha, Zhiyong Lu, Aidong Zhang
CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models,
NeurIPS 2026 Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang
Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models,
Main paper in EMNLP 2026. Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang
Retrieving Counterfactuals Improves Visual In-Context Learning, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026),
Denver CO, June 3 - 7, 2026. Guangzhi Xiong, Sanchit Sinha, Zhenghao He, and Aidong Zhang
Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders,
The Fourteenth International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil, April 23-27, 2026. Guangzhi Xiong, Sanchit Sinha,and Aidong Zhang
Concept‑RuleNet: Grounded Multi‑Agent Neurosymbolic Reasoning in Vision Language Models, The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 26), Singapore, January 20 – January 27, 2026. (Oral) Sanchit Sinha, Guangzhi Xiong, Zhenghao He, Aidong Zhang
Rectifying Shortcut Behaviors in Preference-based Reward Learning,
NeurIPS 2025, San Diego, Dec 2-7, 2025. Wenqian Ye, Guangtao Zheng, Aidong Zhang
COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision-Language Models, the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) (Main conference paper). Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang,
NeuronTune: Towards Self-Guided Spurious Bias Mitigation, International Conference on Machine Learning (ICML 2025), Vancouver, Canada on July 13-19, 2025. Guangtao Zheng, Wenqian Ye, Aidong Zhang
FedMBridge: Bridgeable Multimodal Federated Learning,International conference on machine learning (ICML2024), Vienna, Austria, July 21-27, 2024 (oral presentation) Jiayi Chen and Aidong Zhang
Medcalc-bench: Evaluating large language models for medical calculations. NeurIPS 2024 Track Datasets and Benchmarks, 2024. Nikhil Khandekar, Qiao Jin, Guangzhi Xiong, Soren Dunn, Serina S Applebaum, Zain Anwar, Maame SarfoGyamfi, Conrad W Safranek, Abid A Anwar, Andrew Zhang, Aidan Gilson, Maxwell B Singer, Amisha Dave, Andrew Taylor, Aidong Zhang, Qingyu Chen, and Zhiyong Lu
CoLiDR: Concept Learning using Aggregated Disentangled Representations, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain, August 25-29, 2024. Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang
Benchmarking Retrieval-Augmented Generation for Medicine, the ACL Findings, 2024. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang
On the Role of Server Momentum in Federated Learning, AAAI 2024 Jianhui Sun, Xidong Wu, Heng Huang, Aidong Zhang
On Disentanglement of Asymmetrical Knowledge Transfer for Modality-task Agnostic Federated Learning, AAAI 2024 Jiayi Chen and Aidong Zhang
AdvST: Revisiting Data Augmentations for Single Domain Generalization, AAAI 2024 Guangtao Zheng, Mengdi Huai, Aidong Zhang
Solving a Class of Non-Convex Minimax Optimization in Federated Learning, NeurIPS 2023 Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang
Federated Conditional Stochastic Optimization, NeurIPS 2023 Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang
On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness, Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach, CA, USA, August 6-1 Jiayi Chen and Aidong Zhang
Understanding and Enhancing Robustness of Concept-based Models, the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023) Sanchit Sinha, Mengdi Huai, Jianhui Sun, Aidong Zhang
CLEAR: Generative Counterfactual Explanations on Graphs, NeurIPS 2022 Conference, New Orleans, November 28 -- December 9, 2022 Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li
Towards Interpretation of Pairwise Learning, the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang
HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness, the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020) Jiayi Chen and Aidong Zhang
Metric Learning on Healthcare Data with Incomplete Modalities, the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, August 10-16, 2019 Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Jing Gao, and Aidong Zhang
Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm, the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, August 10-16, 2019 Mengdi Huai, Hongfei Xue, Chenglin Miao, Liuyi Yao, Lu Su, Changyou Chen, and Aidong Zhang

Awards

Thomas M. Linville Endowed Professorship 2023
Fellow -- American Institute for Medical and Biological Engineering (AIMBE) 2021
ACM Fellow 2017
IEEE Fellow 2009
SUNY Distinguished Professor 2014
UB Distinguished Professor 2012
National Science Foundation CAREER Award 1998

Featured Grants & Projects

NIH Project Continually Adaptive Machine Learning Platform for Personalized Biomedical Literature Curation and Exploration
NIH Project Fairness and Robust Interpretability of Prediction Approaches for Aging and Alzheimer’s Disease
NSF Project SCH: Personal Determinants of Health Enhanced Machine Learning Models for Early Prediction of Alzheimer’s Disease and Related Dementias
NSF Project An Explainable Machine Learning Platform for Single Cell Data Analysis
Read More
NSF Project Collaborative Research: PPoSS: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems
Read More
NSF Project A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning
Read More
NSF Project Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis
Read More
NSF: project NSF: Collaborative Research: Mining and Leveraging & Knowledge Hypercubes for Complex Applications
Read More
NSF HDR Project Collaborative Research: Knowledge Guided Machine Learning: A Framework for Accelerating Scientific Discovery
Read More
NSF Project Multimodal Machine Learning for Data with Incomplete Modalities
Read More

In the News

  • UVA Machine Learning Research Team Earns Best Paper Award at KDD 2025

    A UVA team’s article on their method for making AI models fairer, more accurate and more reliable across real-world applications was selected as the conference’s strongest paper.

    Read Now

  • UVA’s 2024 Research Achievement Awards Feature Numerous Engineering Faculty

    Five engineering faculty won three of the University of Virginia’s top research awards. Many more were recognized during an annual ceremony celebrating UVA’s research accomplishments.

    Read Now

  • Community Servers: Largest Computing Society Says 2 UVA Profs Among 7 Best

    The Association for Computing Machinery, or ACM, recognized computer scientists Aidong Zhang and Jack Davidson for above-and-beyond service to the organization and their field. 

    Read Now