AIDONG ZHANG

Thomas M. Linville Professor
Computer Science, Biomedical Engineering, and Data Science

Rice Hall 509
85 Engineer's Way
Charlottesville, Virgnia 22904

434-243-4449

aidong@virginia.edu

Selected Conference Publi­cations

For all publications, visit here.

2025

  • Rectifying Shortcut Behaviors in Preference-based Reward Learning, NeurIPS 2025, San Diego, Dec 2-7, 2025. Wenqian Ye, Guangtao Zheng, Aidong Zhang
  • HyHG: A Temporal Hypergraph Contrastive Learning Framework for Biomedical Hypothesis Generation, the 25th IEEE International Conference on Data Mining (IEEE ICDM 2025), Washington DC, USA, November 12-15, 2025. Amir Shariatmadari, Sikun Guo, Nathan Sheffield, Aidong Zhang, and Kishlay Jha
  • Boosting Clinical Outcome Prediction with Context-Aware Feature Imputation and Disentanglement, the 25th IEEE International Conference on Data Mining (IEEE ICDM 2025), Washington DC, USA, November 12-15, 2025. Lei Gong, Aidong Zhang, and Kishlay Jha
  • LifelongSkill: Toward Modality-varying Lifelong Learning with Latent Knowledge Hypergraph, the 25th IEEE International Conference on Data Mining (IEEE ICDM 2025), Washington DC, USA, November 12-15, 2025. Jiayi Chen, Kishlay Jha, and Aidong Zhang
  • InfAL: Inference Time Adversarial Learning for Improving Research Ideation, The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China, November 5-9, 2025. (EMNLP Finding paper) Sikun Guo, Amir Hassan Shariatmadari, Peng Wang, Albert Huang, 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), Suzhou, China, November 5-9, 2025. (Main conference paper) Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang
  • Leveraging Social Determinants of Health (SDoH) Knowledge Graph to Identify Latent Patterns in Veteran Suicide Risk, IEEE-EMBS International Conference on Biomedical and Health Informatics 2025, (BHI 2025), Atlanta, Georgia, October 26-29, 2025. Chuming Chen, Fahmida Liza Piya, Joshua A. Rolnick, Suzanne A. Milbourne, Cathy Wu, Thomas M. Powers, Jonathan Sanchez-Garcia, Vinod Aggarwal, Aidong Zhang, Rahmatollah Beheshti
  • What is the Visual Cognition Gap between Humans and Multimodal LLMs? The Conference on Language Modeling (COLM 2025), Palais des Congrès, Canada, October 7-10, 2025. Xu Cao, Yifan Shen, Bolin Lai, Wenqian Ye, Yunsheng Ma, Joerg Heintz, Jintai Chen, Meihuan Huang, Jianguo Cao, Aidong Zhang, James Matthew Rehg
  • IdeaBench: Benchmarking Large Language Models for Research Idea Generation, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2025), Toronto, August 3-7, 2025 Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong, Albert Huang, Myles Kim, Corey M. Williams, Stefan Bekiranov, Aidong Zhang
  • Humans and Large Language Models in Clinical Decision Support: A Study with Medical Calculators, The AMIA 2025 Annual Symposium, Atlanta Marriott Marquis, November 15 - 19, 2025. Nicholas Wan, Jin Qiao, Joey Chan, Guangzhi Xiong, Serina Applebaum, Aidan Gilson, Reid McMurry, R. Andrew Taylor, Aidong Zhang, Qingyu Chen, Zhiyong Lu
  • GCAV: A Global Concept Activation Vector Framework for Cross-Layer Consistency in Interpretability, International Conference on Computer Vision (ICCV 2025), Honolulu, Hawai'i, Oct 19 – 23th, 2025. Zhenghao He, Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang
  • MedCite: Can Language Models Generate Verifiable Text for Medicine? ACL 2025 Findings, the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria, July 27 -- August 1st, 2025. Nicholas Wan, Jin Qiao, Joey Chan, Guangzhi Xiong, Serina Applebaum, Aidan Gilson, Reid McMurry, R. Andrew Taylor, Aidong Zhang, Qingyu Chen, Zhiyong Lu
  • Improving Group Robustness on Spurious Correlation via Evidential Alignment, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2025), Toronto, August 3-7, 2025 (Best paper award) Wenqian Ye, Guangtao Zheng, 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
  • ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers, the 34th International Joint Conference on Artificial Intelligence (IJCAI2025), Montreal, Aug 16-22, 2025. Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang
  • Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models, the 34th International Joint Conference on Artificial Intelligence (IJCAI2025), Montreal, Aug 16-22, 2025. Guangzhi Xiong, Eric Xie, Corey Williams, Myles Kim, Amir Shariatmadari, Sikun Guo, Stefan Bekiranov, Aidong Zhang
  • ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models, the 34th International Joint Conference on Artificial Intelligence (IJCAI2025), Montreal, Aug 16-22, 2025. Guangtao Zheng, Wenqian Ye, Aidong Zhang
  • Optimizing External and Internal Knowledge of Foundation Models for Scientific Discovery, SIAM International Conference on Data Mining (SDM25), Alexandria Virginia, U.S., May 1-3, 2025. Sikun Guo, Guangzhi Xiong, and Aidong Zhang
  • Integrating Social Determinants of Health in a Multi-Modal Deep Clustering Survival Model for Injury-Risk in Alzheimer’s and Related Dementia Patients, AAAI Bridge Program 2025: AI for Medicine and Healthcare, Philadelphia, PA USA, February 25, 2025. Kazi Noshin, Mary Regina Boland, Bojian Hou, Weiqing He, Victoria Lu, Li Shen, Aidong Zhang
  • Understanding the Clinical Modalities Important in NeuroDegenerative Disorders, Alzheimer's Disease, and Risk of Patient Injury Using Machine Learning and Survival Analysis, AMIA 2025 Informatics Summit, March 10-13, 2025, Pittsburgh. PMCID: PMC12150751 PMID: 40502273. Kazi Noshin, Mary Regina Boland, Bojian Hou, Weiqing He, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang
  • Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions, PACIFIC SYMPOSIUM ON BIOCOMPUTING (PSB), Hawaii, January 04-08, 2025. Guangzhi Xiong, Qiao Jin, Xiao Wang, Minjia Zhang, Zhiyong Lu, Aidong Zhang
  • Uncovering Important Diagnostic Features for Alzheimer’s, Parkinson’s and Other Dementias Using Interpretable Association Mining Methods. PACIFIC SYMPOSIUM ON BIOCOMPUTING (PSB), Hawaii, January 04-08, 2025. Kazi Noshin, Mary Regina Boland, Bojian Hou, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang

2024

  • Context-specific Feature Augmentation for Improving Social Determinants of Health Extraction, IEEE International Conference on Big Data (IEEE BigData), December 14-19, 2024. Lei Gong, Andrey Shor, Aidong Zhang, and Kishlay Jha
  • Embracing Foundation Models for Advancing Scientific Discovery, IEEE International Conference on Big Data (IEEE BigData), December 14-19, 2024. Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong, 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
  • Generalizing to Unseen Domains via Text-guided Augmentation: A Training-free Approach, the 18th European Conference on Computer Vision (ECCV2024), Sep 29th - Oct 4th, 2024, Milano, Italy. Daiqing Qi, Handong Zhao, Aidong Zhang, and Sheng Li
  • Benchmarking Spurious Bias in Few-Shot Image Classifiers, the 18th European Conference on Computer Vision (ECCV2024), Sep 29th - Oct 4th, 2024, Milano, Italy. 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
  • Spuriousness-Aware Meta-Learning for Learning Robust Classifiers, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain, August 25-29, 2024. Guangtao Zheng, Wenqian Ye, Aidong Zhang
  • Benchmarking Retrieval-Augmented Generation for Medicine, the ACL Findings, 2024. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang
  • MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain, August 25-29, 2024. Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni, Jianhua Lu, and Aidong Zhang
  • 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
  • A Self-explaining Neural Architecture for Generalizable Concept Learning, the 33rd International Joint Conference on Artificial Intelligence (IJCAI2024), Jeju Aug 3-9, 2024. Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang
  • Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation, the 33rd International Joint Conference on Artificial Intelligence (IJCAI2024), Jeju Aug 3-9, 2024. Guangtao Zheng, Wenqian Ye, and 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, and Aidong Zhang
  • On the Role of Server Momentum in Federated Learning, AAAI 2024. Jianhui Sun, Xidong Wu, Heng Huang, and Aidong Zhang

2023

  • Solving a Class of Non-Convex Minimax Optimization in Federated Learning, NeurIPS 2023 Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang
  • On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval, Findings of the Association for Computational Linguistics: EMNLP 2023 Jiayi Chen, Hanjun Dai, Bo Dai, Aidong Zhang, Wei Wei
  • 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 6-10, 2023. Jiayi Chen and Aidong Zhang
  • Enhance Diffusion to Improve Robust Generalization, Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach, CA, USA, August 6-10, 2023. Jianhui Sun, Sanchit Sinha, Aidong Zhang
  • Learning for Counterfactual Fairness from Observational Data, Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach, CA, USA, August 6-10, 2023. Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li
  • Learning to Learn Task Transformations for Improved Few-Shot Classification, SIAM International Conference on Data Mining (SDM23), Graduate Minneapolis Hotel | Minneapolis, Minnesota, U.S., April 27 - 29, 2023. Guangtao Zheng, Qiuling Suo, Mengdi Huai, Aidong Zhang
  • Understanding and Enhancing Robustness of Concept-based Models, the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023), Washington, DC, February 7-14, 2023. Sanchit Sinha, Mengdi Huai, Jianhui Sun, and Aidong Zhang

2022

  • 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 Automating Model Explanations with Certified Robustness Guarantee, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-2022), Vancouver Convention Centre, Canada, Feb 21-28, 2022. Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao, Aidong Zhang
  • Demystify Hyperparameters for Stochastic Optimization with Transferable Representations, Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022) Jianhui Sun, Mengdi Huai, Kishlay Jha, and Aidong Zhang
  • FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks, Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022) Jiayi Chen and Aidong Zhang
  • Towards Automating Model Explanations with Certified Robustness Guarantee, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-2022) Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao, Aidong Zhang

2021

  • Knowledge-Guided Efficient Representation Learning for Biomedical Domain, Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021) Kishlay Jha, Guangxu Xun, Nan Du, and Aidong Zhang
  • A Stagewise Hyperparameter Scheduler to Improve Generalization, Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021), Singapore, August 14-18, 2021. Jianhui Sun, Ying Yang, Guangxu Xun, and Aidong Zhang

2020

  • Malicious Attacks against Deep Reinforcement Learning Interpretations, the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020) Mengdi Huai, Jianhui Sun, Renqin Cai, Liuyi Yao and Aidong Zhang
  • Towards Interpretation of Pairwise Learning, the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang
  • CorNet: Correlation Networks for Extreme Multi-label Text Classification, the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020) Guangxu Xun, Kishlay Jha, Jianhui Sun and Aidong Zhang
  • Task-Adaptive Graph Meta-learning, the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020) Qiuling Suo, Jingyuan Chou, Weida Zhong and 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
  • Pairwise Learning with Differential Privacy Guarantees, the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang

2019

  • 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
  • Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts, the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), Alaska, USA, August 4-8, 2019. Kishlay Jha, Guangxu Xun, Yaqing Wang, 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
  • Semi-supervised Few-shot Learning for Disease Type Prediction, the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, January 27 – February 1, 2019. Tianle Ma and Aidong Zhang

2018

  • Representation Learning for Treatment Effect Estimation from Observational Data, Thirty-second Conference on Neural Information Processing Systems (NIPS2018), Montréal, Canada, December 3-8, 2018. Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang
  • Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution, the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, August 19-23, 2018. Kishlay Jha, Guangxu Xun, Yaqing Wang, Vishrawas Gopalakrishnan, and Aidong Zhang
  • Metric Learning from Probabilistic Labels, the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, August 19-23, 2018. Mengdi Huai, Chenglin Miao, Yaliang Li, Qiuling Suo, Lu Su, and Aidong Zhang
  • Multi-task Sparse Metric Learning on Measuring Patient Similarity Progression, the 2018 IEEE International Conference on Data Mining (ICDM'18), Singapore, November 17-20, 2018. Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Mengdi Huai, and Aidong Zhang