Bio

PhD, Purdue 1994

 

Research interests include:

Machine Learning, Data Mining, Bioinformatics, Health Informatics

 

 

Aidong 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.

Awards

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

Research Interests

  • Machine Learning
  • Data Mining
  • Bioinformatics
  • Health Informatics

Selected Publications

  • 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
  • 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

Featured Grants & Projects

  • NSF: Medium Project


    NSF: Medium: High-Dimensional Interaction Analysis in Bio-Data Sets

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  • NSF: Medium project


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

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  • NSF Eager Project


    EAGER: Toward Interpretation of Pairwise Learning

  • NSF HDR Project


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

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  • NSF Small Project


    Multimodal Machine Learning for Data with Incomplete Modalities