Bio

PhD, Purdue 1994

Aidong Zhang develops machine learning and data science approaches to modeling and analysis of structured and unstructured data with a variety of applications, especially biomedical applications. Dr. Zhang is a William Wulf Faculty Fellow and 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.

Awards

  • ACM Fellow 2017
  • IEEE Fellow 2009
  • SUNY Distinguished Professor 2014
  • UB Distinguished Professor 2012
  • National Science Foundation CAREER Award 1998

Research Interests

  • Machine Learning
  • Data Mining
  • Bioinformatics
  • Health Informatics

In the News

Selected Publications

  • 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
  • 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
  • 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
  • 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
  • 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

Featured Grants & Projects

  • NSF


    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

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  • NSF Project


    Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis

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  • NSF: project


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

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  • NSF HDR Project


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

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  • NSF Project


    Multimodal Machine Learning for Data with Incomplete Modalities

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