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Ninghao Liu

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Assistant Professor of Computer Science

Dr. Ninghao Liu's research interests are Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection. He has published refereed papers at recognized venues such as KDD, WWW, ICML, ICLR, NeurIPS, WSDM, IJCAI, CIKM, ICDM, etc. His work won the Outstanding Paper Award in ICML 2022, the Best Paper Award Shortlist in WWW 2019, and the Best Paper Award Candidate in ICDM 2019.


Ph.D. in Computer Science, Texas A&M University, 2021

M.S. in Electrical and Computer Engineering, Georgia Institute of Technology, 2015

Research Areas:
Research Interests:

Explainable AI, Graph Analytics, Anomaly detection, Data Mining

Selected Publications:

[1] Xiaotian Han and Zhimeng Jiang and Ninghao Liu and Xia Hu. "G-Mixup: Graph Data Augmentation for Graph Classification." ICML. 2022

[2] Yushun Dong, Ninghao Liu, Brian Jalaian, and Jundong Li. "Edits: Modeling and mitigating data bias for graph neural networks." WWW. 2022.

[3] Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. "Sparse-interest network for sequential recommendation." WSDM. 2021.

[4] Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu. "An embarrassingly simple approach for trojan attack in deep neural networks." KDD. 2020.

[5] Mengnan Du, Ninghao Liu, and Xia Hu. "Techniques for interpretable machine learning." Communications of the ACM. 2019.

[6] Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, and Xia Hu. "Is a single vector enough? exploring node polysemy for network embedding." KDD. 2019.

[7] Ninghao Liu, Hongxia Yang, and Xia Hu. "Adversarial detection with model interpretation." KDD. 2018.

[8] Ninghao Liu, Donghwa Shin, and Xia Hu. "Contextual outlier interpretation." IJCAI. 2018.

Of note:

Outstanding Paper Award, ICML 2022

Best Paper Award Candidate, ICDM 2019

Best Paper Award Shortlist, WWW 2019

Best Paper Award, IEEE ICCC 2014

Articles Featuring Ninghao Liu

Dr. Ninghao Liu and his collaborator in University of Virginia received a three-year grant titled "Graph-Oriented Usable Interpretation" from the National Science Foundation (NSF).

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