Aidong Zhang

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

Contact

aidong@virginia.edu

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, federated learning, and generative AI. She also works 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 Department of Biomedical Engineering and School of Data Science at University of Virginia. Her research interests focus on machine learning, data mining, bioinformatics and health informatics.

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

Research Interests

Machine Learning

Data Mining

Bioinformatics

Health Informatics

Selected Publications

FedMBridge: Bridgeable Multimodal Federated Learning,International conference on machine learning (ICML2024), Vienna, Austria, July 21-27, 2024 (oral presentation) Jiayi Chen and Aidong Zhang

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

Benchmarking Retrieval-Augmented Generation for Medicine, the ACL Findings, 2024. Guangzhi Xiong, Qiao Jin, Zhiyong 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

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

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

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 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

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

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

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

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

NSF Project An Explainable Machine Learning Platform for Single Cell Data Analysis

NSF Project Collaborative Research: PPoSS: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems

NSF Project A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning

NSF Project Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis

NSF: project NSF: Collaborative Research: Mining and Leveraging & Knowledge Hypercubes for Complex Applications

NSF HDR Project Collaborative Research: Knowledge Guided Machine Learning: A Framework for Accelerating Scientific Discovery

NSF Project Multimodal Machine Learning for Data with Incomplete Modalities