About Me

I am a full-time researcher in the Department of Data Science & Systems Research at NEC Laboratories, America. I received my Ph.D. degree from the Department of Computer Science at University of California, Santa Barbara.

My current research focuses on hard problems in mining and learning from complex data with high dimensionality, such as dynamic graphs, sequence data, and time series data, which are prevalent in real-life applications.

Here is my CV.

Bo Zong


2018/04/26 Our paper LogLens: A Real-time Log Analysis System is accepted by ICDCS'18.    
2018/01/29 Our paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection is accepted by ICLR'18.    
2017/11/22 Our paper Substructure Assembling Network for Graph Classification is accepted by AAAI'18.                

Selected Publications

(Publications marked with '*' order authors alphabetically.)
  1. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection, ICLR'18. [pdf]

  2. Xiaohan Zhao, Bo Zong, Ziyu Guan, Kai Zhang, and Wei Zhao. Substructure Assembling Network for Graph Classification, AAAI'18. [pdf]

  3. Bo Zong, Xusheng Xiao, Zhichun Li, Zhenyu Wu, Zhiyun Qian, Xifeng Yan, Ambuj K. Singh, and Guofei Jiang. Behavior Query Discovery in System-Generated Temporal Graphs, VLDB'16 . [pdf] [full version] [slides]

  4. Bo Zong, Yinghui Wu, Jie Song, Ambuj K. Singh, Hasan Cam, Jiawei Han, and Xifeng Yan. Towards Scalable Critical Alert Mining, KDD'14. [slides] [pdf]

  5. Bo Zong, Ramya Raghavendra, Mudhakar Srivatsa, Xifeng Yan, Ambuj K. Singh, and Kang-Won Lee. Cloud Service Placement via Subgraph Matching, ICDE'14. [slides] [pdf]

Contact Information

Bo Zong
4 Independence Way #200
Princeton, NJ 08540
bzong [at) nec-labs dot com

keywords: Bo Zong, Graph learning, Deep Learning, Graph Neural Networks, Big Data, Big Graph, Graph analytics, Data Mining, Graph Mining, Dynamic Graphs, Network Dynamics, Temporal Graphs