Zhengzhang (Zach) Chen

Zhengzhang (Zach) Chen

Senior Researcher

Data Science & System Security Department

NEC Laboratories America, Inc.

4 Independence Way, Princeton, NJ 08540

E-mail: zchen [at] nec-labs.com; zhengzhang.chen [at] gmail.com

WHO AM I

Welcome to my homepage! I am a senior research scientist in Data Science & System Security Department at NEC Laboratories America. Before coming to NEC, I was a Research Assistant Professor, working with Professor Alok Choudhary, in the Department of Electrical Engineering and Computer Science at Northwestern University. I received my Ph.D. from the Department of Computer Science at North Carolina State University. My adviser is Professor Nagiza F. Samatova. 

My research interests are in the areas of large-scale data mining, artificial intelligence, and machine learning including: graph mining, graph neural network, anomaly detection, transfer learning, spatio-temporal data mining, and their applications in computer security, IoT/OT system analytics, social media analytics, climate science, biology, material science, finance, user behavior analytics, and etc. 

Selected Publications 

Book Chapter

• Kanachana Padmanabhan, Zhengzhang Chen,  Sriram Lakshminarasimhan, Siddarth S. Ramaswamy, and Bryan T. Richardson, “Graph-based Anomaly Detection," in Practical Data Mining with R, CRC Press, 2013.

Peer-Reviewed Conference and Workshop Papers

• Zhengzhang Chen*, Dongjie Wang*, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen. Interdependent Causal Networks for Root Cause Localization. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023. (*Equal Contribution)

• Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, and Haifeng Chen. Incremental Causal Graph Learning for Online Root Cause Analysis. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023.

• Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, and Yan Liu. Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning. In proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.

• Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Jingchao Ni, Denghui Zhang, Haifeng Chen, and Xia Hu. Towards Learning Disentangled Representations for Time Series. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2022.

• Shen Wang, Zhengzhang Chen, Jingchao Ni, Haifeng Chen, and Philip S. Yu. Towards Robust Graph Neural Networks via Adversarial Contrastive Learning. In Proceedings of the IEEE International Conference on Big Data (BigData), 2022.

• Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, and Haifeng Chen. Superclass-Conditional Gaussian Mixture Model for Learning Fine-Grained Embeddings. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. (Spotlight Presentation, 5%). 

• Junheng Hao, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Haifeng Chen, Junghwan Rhee, Zhichun Li, and Wei Wang. Multi-source Inductive Knowledge Graph Transfer. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2022.

• Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, and Yevgeniy Vorobeychik, "FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems," in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

• Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, and Jiliang Tang, "Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection, " in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2021.

• Jingchao Ni, Zhengzhang Chen, Wei Cheng, Bo Zong, Dongjin Song, Yanchi Liu, Xuchao Zhang, and Haifeng Chen,  "Interpreting Convolutional Sequence Model by Learning Local Prototypes with Adaptation Regularization," in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

• Lei Cai, Zhengzhang Chen,  Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li, and Haifeng Chen, "Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs," in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

• Xinyang Feng, Dongjing Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen, "Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection," in Proceedings of the 29th ACM International Conference on Multimedia (ACMMM), 2021.

• Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and XiaHu, "AutoOD: Neural Architecture Search for Outlier Detection," in Proceedings of the 37th IEEE International Conference on Data Engineering (ICDE), Crete, Greece, 2021. [PDF]

• Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, and Susan Davidson, "Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series," in Proceedings of the 35th AAAI Conference on Advance of Artificial Intelligence (AAAI), 2021. 

• Pengyang Wang, Jiaping Gui, Zhengzhang Chen, Junghwan Rhee, Haifeng Chen, and Yanjie Fu, “A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement,” in Proceedings of the Web Conference 2020 (WWW), Taiwan, China, 2020.

• Qi Wang, Wajih Ul Hassan, Ding Li, Kangkook Jee, Xiao Yu, Kexuan Zou, Junghwan Rhee, Zhengzhang Chen, Wei Cheng, Carl A. Gunter, Haifeng Chen, "You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis, " in Proceedings of the Network and Distributed System Security Symposium (NDSS), San Diego, California, USA, 2020.

• Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, and Gerard de Melo, “Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-based Recommendation,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020.

• Jiaping Gui, Ding Li, Zhengzhang Chen, Junghwan Rhee, Xusheng Xiao, Mu Zhang, Kangkook Jee, Zhichun Li, Haifeng Chen, "APTrace: A Responsive System for Agile Enterprise Level Causality Analysis," in Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE), Dallas, Texas, USA, 2020.

• Denghui Zhang, Yanchi Liu, Wei Cheng, Bo Zong, Jingchao Ni, Zhengzhang Chen, Haifeng Chen, and Hui Xiong, "T2-Net: A Semi-supervised Deep Model for Turbulence Forecasting," in Proceedings of the IEEE International Conference on Data Mining (ICDM), 2020.

• Wajih Ul Hassan, Ding Li, Kangkook Jee, Xiao Yu, Kexuan Zou, Dawei Wang, Zhengzhang Chen, Zhichun Li, Junghwan Rhee, Jiaping Gui, and Adam Bates, “This is Why We Can’t Cache Nice Things: Lightning-Fast Threat Hunting Using Suspicion-Based Hierarchical Storage,” in Proceedings of Annual Computer Security Applications Conference (ACSAC), 2020. 

• Zhengzhang Chen*,  Shen Wang*, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Yu, "Heterogeneous Graph Matching Networks for Unknown Malware Detection," in Proceedings of the 28th International Joint Conference on Artificial Intelligence  (IJCAI), Macao, China, August 2019.  (*Equal Contribution)

• Shen Wang, Zhengzhang Chen, Ding Li, Zhichun Li, Lu-An Tang, Jingchao Ni, Junghwan Rhee,  Haifeng Chen, and Philip S. Yu,  "Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification ," in Proceedings of the 19th SIAM International Conference on Data Mining (SDM), Alberta, Canada, May 2019. [PDF]

• Wajih Ul Hassan, Shengjian Guo, Ding Li, Zhengzhang Chen, Kangkook Jee, Zhichun Li, and Adam Bates, "NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage," in Proceedings of  the 26th ISOC Network and Distributed System Security Symposium (NDSS 2019). San Diego, CA, USA, 2019.

• Zhengzhang Chen*, Chen Luo*, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, and Jieping Ye, "TINET: Learning Invariant Networks via Knowledge Transfer," in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), London, 2018.  (*Equal Contribution)

• Zhengzhang Chen*, Cheng Cao*, James Caverlee, Lu-An Tang, Chen Luo, and Zhichun Li, "Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks," In Proceedings of  the 27th ACM International Conference on Information and Knowledge Management (CIKM), Turin, Italy, 2018. (*Equal Contribution)

• Zhengzhang Chen*, Ying Lin*, Cheng Cao, Lu-An Tang, Kai Zhang, Wei Cheng, and Zhichun Li, "Collaborative Alert Ranking for Anomaly Detection," in Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), Turin, Italy, 2018. (*Equal Contribution)

• Yusheng Xie, Zhengzhang Chen, Ankit Agrawal and Alok Choudhary, “Distinguish Polarity in Bag-of-words Model Visualization with Regularized Concentration,” in Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, USA, 2017. 

• Zhengzhang Chen*, Boxiang Dong*, Hui (Wendy) Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, and Haifeng Chen, "Efficient Discovery of Abnormal Event Sequences in Enterprise Security System," in Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM), Pan Pacific, Singapore, 2017. (*Equal Contribution)

• Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen, and Wei Wang, "Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations," in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Francisco, 2016. (Best Paper Award Runner-Up)

• Kai Zhang, Shandian Shan, Chaoran Cheng, Zhi Wei, Zhengzhang Chen, Haifeng Chen, Guofei Jiang, Yuan Qi, and Jieping Ye, "Annealed Sparsity via Adaptive and Dynamic Shrinking," in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Francisco, 2016. 

• Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang, "Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events," in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) New York, July 2016.

• Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Haifeng Chen, and Guofei Jiang, "Integrating Community and Role Detection in Information Networks," in Proceedings of the 16th SIAM International Conference on Data Mining (SDM), Miami, FL, May 2016.

• Kai Zhang, Qiaojun Wang, Zhengzhang Chen, Ivan Marsic, Vipin Kumar, and Geoff Jiang, “From Categorical to Numerical: Multiple Transitive Distance Learning and Embedding,” in Proceedings of the 15th SIAM International Conference on Data Mining (SDM), Vancouver, British Columbia, Canada, 2015. 

• Chen Jin, Zhengzhang Chen, William Hendrix, Md. Mostofa Ali Patwary, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary,  “Incremental, Distributed Single-Linkage Hierarchical Clustering Algorithm Using MapReduce,” in Proceedings of the 23rd High Performance Computing Symposium (HPC), Alexandria, VA, USA, 2015. 

• Zhengzhang Chen, Seung Woo Son, William Hendrix, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary,  “NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing,” in Proceedings of International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), New Orleans, Louisiana, USA 2014. [PDF | BibTex]

• Yusheng Xie, Zhengzhang Chen, Diana Palsetia, Ankit Agrawal, and Alok Choudhary,  “Indexing Bipartite Memberships in Web Graphs,” in Proceedings of The IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), Beijing, China, 2014. 

• Yu Cheng, Zhengzhang Chen, Hongliang Fei, Fei Wang, and Alok Choudhary, “Batch Mode Active Learning with Hierarchical-Structured EmbeddedVariance,” in Proceedings of 14th SIAM International Conference on Data Mining (SDM 2014), Philadelphia, Pennsylvania, USA, 2014. 

• Yu Cheng, Zhengzhang Chen, Lu Liu, Jiang Wang, Ankit Agrawal, and Alok Choudhary,  “Feedback-Driven Multiclass Active Learning for Data Streams,” in Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), San Francisco, USA, 2013. [PDF | BibTex]

• Yu Cheng, Zhengzhang Chen, Kunpeng Zhang, Jiang Wang, Ankit Agrawal, and Alok Choudhary,  “Bootstrapping Active Name Disambiguation with Crowdsourcing,”in Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), San Francisco, USA, 2013. [PDF | BibTex]

• Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, and Alok Choudhary,  “Random Walk-based Graphical Sampling in Unbalanced Heterogeneous Bipartite Social Graphs,” in Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), San Francisco, USA, 2013. [PDF | BibTex]

• Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, and Alok Choudhary, “Elver: Recommending Facebook Pages in Cold Start Situation Without Content Features,” in Proceedings of IEEE International Conference on Big Data (BigData), Santa Clara, CA, USA, 2013. [PDF | BibTex]

• Zhengzhang Chen, Yusheng Xie, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Wei-keng Liao, Nagiza F. Samatova, and Alok Choudhary, “Forecast Oriented Classification of Spatio-Temporal Extreme Events,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, August 2013. [PDF | BibTex]

• Zhengzhang Chen*, Yusheng Xie*, Kunpeng Zhang, Yu Cheng, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary, “Detecting and Tracking Disease Outbreaks in Real-time through Social Media,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, August 2013. (*Both authors contributed equally) [PDF | BibTex]

• Yu Cheng, Yusheng Xie, Zhengzhang Chen, Songtao Guo, Ankit Agrawal, and Alok Choudhary, “JobMiner: A Real-time System for Mining Job-related Patterns from Social Media,” in Proceedings of the 19th ACM SIGKDD Conference on Knowledge, Discovery and Data Mining (SIGKDD) to be held in Chicago, USA, on August 11-14, 2013. [PDF | BibTex]

• Kunpeng Zhang, Doug Downey, Zhengzhang Chen, Yusheng Xie, Yu Cheng, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary, “A Probabilistic Graphical Model for Brand Reputation Assessment in Social Networks,” in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) to be held in Niagara Falls, Canada, August 25-28, 2013. [PDF | BibTex]

• Zhengzhang Chen, John Jenkins, Jinfeng Rao, Alok Choudhary, Fredrick Semazzi, Anatoli V. Melechko, Vipin Kumar, and Nagiza F. Samatova, “Automatic Detection and Correction of Multi-class Classification Errors Using System Whole-part Relationships,” in Proceedings of the 13th SIAM International Conference on Data Mining (SDM), Austin, Texas, USA, May 2-4, 2013. [PDF | BibTex]

• Yusheng Xie, Zhengzhang Chen, Kunpeng Zhang, Md. Mostofa Ali Patwary, Yu Cheng, Haotian Liu, Ankit Agrawal, and Alok Choudhary, “Graphical Modeling of Macro Behavioral Targeting in Social Networks,” in Proceedings of the 13th SIAM International Conference on Data Mining (SDM), Austin, Texas, USA, May 2-4, 2013.  [PDF | BibTex]

• Zhengzhang Chen*, Huseyin Sencan*, William Hendrix, Tatdow Pansombut, Frederick Semazzi, Alok Choudhary, Vipin Kumar, Anatoli V. Melechko, and Nagiza F. Samatova, “Classification of Emerging Extreme Event Tracks in Multi-Variate Spatio-Temporal Physical Systems Using Dynamic Network Structures: Application to Hurricane Track Prediction,” In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), pages 1478-1484, Barcelona, Spain, July, 2011. (*Both authors contributed equally) [PDF | BibTex]

         This research was highlighted by the DOE ASCR Discovery web-magazine.

Journal Articles

• Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu, "Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning," IEEE Transactions on Neural Networks and Learning Systems, 33(6): 2365-2377 (2022).

• Boxiang Dong, Zhengzhang Chen, Hui (Wendy) Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, and Haifeng Chen, "Anomalous Event Sequence Detection," IEEE Intelligent Systems, 36(3): 5-13 (2021).

• Yusheng Xie, Zhengzhang Chen, Diana Palsetia, Goce Trajcevski, Ankit Agrawal, and Alok Choudhary, "Silverback+: Scalable Association Mining Via Fast List Intersection For Columnar Social Data," Knowledge and Information Systems, 50(3): 969-997 (2017). [PDF]

• Qiaojun Wang, Kai Zhang, Zhengzhang Chen, Dequan Wang, Guofei Jiang, and Ivan Marsic, "Enhancing semi-supervised learning through label-aware base kernels," Neurocomputing, 2016. [PDF]

• Ruoqian Liu, Abhishek Kumar, Zhengzhang Chen, Ankit Agrawal, Veera Sundararaghavan, and Alok Choudhary, “A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design,” Nature Scientific Reports, vol. 5, no. 11551, 2015.  [PDF]

• Seung Woo Son, Zhengzhang Chen, William Hendrix, Ankit Agrawal, Wei-keng Liao, and Alok Choudhary, “Data Compression for the Exascale Computing Era–Survey,” Supercomputing Frontiers and Innovations, 2014.  [PDF]

• Wenbin Chen, Zhengzhang Chen, Nagiza F. Samatova, Lingxi Peng, Jianxiong Wang, Maobin Tang, “Solving the Maximum Duo-preservation String Mapping Problem with Linear Programming,” Theoretical Computer Science, 2014. [PDF | BibTex]

• Yusheng Xie, Zhengzhang Chen, Kunpeng Zhang, Yu Cheng, Danial K. Honbo, Ankit Agrawal, and Alok Choudhary, “MuSES: a Multilingual Sentiment Elicitation System for Social Media Data,” IEEE Intelligent Systems, vol. 99, 2013. [PDF | BibTex]

• Zhengzhang Chen, William Hendrix, Hang Guan, Isaac K. Tetteh, Alok Choudhary, Fredrick Semazzi, Nagiza F. Samatova. “Discovery of Extreme Events-Related Communities In Contrasting Groups of Physical System Networks,” Data Mining and Knowledge Discovery, vol. 27(2): 225-258, 2013. [PDF | BibTex] 

        This research was highlighted by the NSF News, DOE Research News, Science360 News Service, Computing Research Highlight of the Week, The Conversation, and Esciencenews. An extended press at NC State News and McCormick News are available. 

• Zhengzhang Chen, Kanchana Padmanabhan, Andrea M Rocha, Yekaterina Shpanskaya, James R Mihelcic, Kathleen Scott, and Nagiza F. Samatova, “SPICE: Discovery of Phenotype-Determining Component Interplays,” BMC Systems Biology,  vol. 6(1): 40, 2012. [PDF | BibTex]

• Zhengzhang Chen, William Hendrix, and Nagiza F. Samatova, “Community-based Anomaly Detection in Evolutionary Networks,” Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, vol. 39(1):59-85, 2011. [PDF | BibTex]

• Mathew C Schmidt, Andrea M Rocha, Kanchana Padmanabhan, Zhengzhang Chen, Kathleen Scott, James R. Mihelcic and Nagiza F. Samatova, “Efficient alpha, beta-motif Finder for Identification of Phenotype-related Functional Modules,” BMC Bioinformatics, vol. 12(1):440, 2011. [PDF | BibTex]  

• Wenbin Chen, Dengpan Yin, and Zhengzhang Chen, "Inapproximability Results for Equations over Infinite Groups," Theoretical Computer Science, 411 (26-28): 2513-2519, 2010. [PDF | BibTex] 

Patents (36 granted)

• Y. Chen, Z. Chen, C. Lumezanu, M. Natsumeda, X. Yu, W. Cheng, T. Mizoguchi, and H. Chen, Causal attention-based multi-stream RNN for computer system metric prediction and influential events identification based on metric and event logs, US Patent 11,782,812, issued on 10/2023

• Y. Liu, J. Ni, B. Zong, H. Chen, Z. Chen, W. Cheng, and D. Zhang, Semi-supervised deep model for turbulence forecasting, US Patent 11,650,351, issued on 05/2023

• J. Gui, Z. Chen, J. Rhee, H. Chen, and P. Wang, Flexible edge-empowered graph convolutional networks with node-edge enhancement, US Patent 11,620,492, issued on 04/2023

• Z. Chen, J. Gui, J. Rhee, H. Chen, and S. Wang, Anomaly detection with graph adversarial training in computer systems, US Patent 11,606,389, issued on 03/2023

• Z. Chen, J. Gui, H. Chen, and L. Cai, Structural graph neural networks for suspicious event detection, US Patent 11,522,881, issued on 12/2022

• Z. Chen, D. Li, Z. Li, and S. Wang, Unknown malicious program behavior detection using a graph neural network, US Patent 11,463,472, issued on 10/2022

• D. Li, K. Jee, Z. Chen, Z. Li, W. Hassan, Automatic threat alert triage through data history, JP Patent 7,101,272, issued on 07/2022

• J. Rhee, L. Tang, Z. Chen, C. Kim, Z. Li, and Z. Zhou, Protocol-independent anomaly detection, JP Patent 7,086,230, issued on 06/2022

• J. Rhee, L. Tang, Z. Chen, C. Kim, Z. Li, and Z. Zhou, Protocol-independent anomaly detection, US Patent 11,297,082, issued on 04/2022

• D. Li, K. Jee, Z. Li, Z. Chen, and X. Yu, Real-time Threat Alert Forensic Analysis, US Patent 11,275,832, issued on 03/2022

• Z. Wu, Y. Li, J. Rhee, K. Jee, Z. Li, J. Kamimura, L. Tang, and Z. Chen, Detection and prevention of

user value-based ransomware, JP Patent 7,011,723, issued on 1/2022

• Z. Wu, Y. Li, J. Rhee, K. Jee, Z. Li, J. Kamimura, L. Tang, and Z. Chen, User-added-value-based

ransomware detection and prevention, US Patent 11,223,649, issued on 1/2022

• D. Li, K. Jee, Z. Chen, Z. Li, W. Hassan, Automated Threat Alert Triage via Data Provenance, US

Patent 11,194,906, issued on 12/2021

• D. Li, K. Jee, Z. Chen, L. Tang, and Z. Li, Inter-application Dependency Analysis for Improving Computer System Threat Detection, US Patent 11,030,308, issued on 6/2021

• L. Tang, Z. Chen, Z. Li, Z. Wu, J. Kamimura, and H. Chen, Graph Model for Alert Interpretation in Enterprise Security System, US Patent #10,915,625, 10,915,626, and 10,885,185, granted on 02/2021

• Z. Chen, L. Tang, Z. Li, and C. Luo, Knowledge Transfer System for Accelerating Invariant Network Learning, US Patent #10,511,613, granted on 12/2019

• Z. Chen, L. Tang, Z. Li, and C. Cao, Behavior-based Community Detection in Enterprise Information Networks, US Patent #10,476,754, granted on 11/2019

• Z. Chen, L. Tang, Z. Li, and C. Cao, Behavior-based Host Modeling, US Patent # 10,476,753, granted on 11/2019

• K. Yoshihira, Z. Li, Z. Chen, H. Chen, G. Jiang, and L. Tang, Graph-based Fusing of Heterogeneous Alerts, JP Patent #6,616,045, granted on 11/2019

• K. Yoshihira, Z. Li, Z. Chen, H. Chen, G. Jiang, and L. Tang, Blue Print Graphs for Fusing of Heterogeneous Alerts, US Patent #10,476,752, granted on 11/2019 

• K. Yoshihira, Z. Li, Z. Chen, H. Chen, G. Jiang, and L. Tang, Graph-based Fusing of Heterogeneous Alerts, US Patent #10,476,749, granted on 11/2019 

• Z. Chen, L. Tang, Z. Li, and C. Cao, Peer-based Abnormal Host Detection for Enterprise Security Systems, US Patent #10,367,842, granted on 07/2019

• L. Tang, Z. Chen, T. Chen, G. Jiang, F. Xu, and H. Chen, Integrating Community and Role Detection in Information Networks, JP Patent #654,581,9, granted on 07/2019

• Z. Chen, L. Tang, Y. Lin, Z. Li, H. Chen, and G. Jiang, Online Alert Ranking and Attack Scenario Reconstruction, US Patent #10,333,952, granted on 06/2019

• Z. Chen, L. Tang, G. Jiang, K. Yoshihira, and H. Chen, Real-time Detection of Abnormal Network Connections in Streaming Data, Two US Patents #10,333,815 and #10,367,838, granted on 06/2019 and 07/2019, respectively

• Z. Chen, L. Tang, B. Dong, G. Jiang, and H. Chen, Graph-based Intrusion Detection Using Process Traces, US Patent #10,305,917 and JP Patent #655,777,4, granted on 05/2019 and 07/2019, respectively

• L. Tang, Z. Chen, K. Zhang, H. Chen, and Z. Li, Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events, US Patent #10,291,483, granted on 05/2019

• L. Tang, H. Zhang, Z. Chen, B. Zong, Z. Li, G. Jiang, and K. Yoshihira, Graph-based Attack Chain Discovery in Enterprise Security Systems, US Patent #10,289,841, granted on 05/2019

• L. Tang, H. Zhang, Z. Chen, B. Zong, Z. Li, G. Jiang, and K. Yoshihira, Constructing Graph Models of Event Correlation in Enterprise Security Systems, US Patent #10,298,607, granted on 05/2019

• K. Zhang, Z. Chen, H. Chen, and G. Jiang, Annealed Sparsity Via Adaptive and Dynamic Shrinking, JP Patent #648,002,2 and US Patent #10,504,040, granted on 03/2019 and 11/2019,  respectively

Selected Services and Awards

• Guest editor for ACM Transaction on Data Science: Special Issue on Retrieving and Learning from Internet of Things Data

• Reviewer for Pattern Recognition, PLOS ONE,  IEEE Transaction on Knowledge and Data Engineering,  IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, IEEE Intelligent Systems, Concurrency and Computation Journal, Journal of Computer and Communications,  IEEE Transactions on Network Science and Engineering, Information Sciences, Applied Computing and Informatics, Journal of Electronic Commerce Research, EURASIP Journal on Bioinformatics and Systems Biology, Current Synthetic & System Biology, Integrating Materials and Manufacturing Innovation

• SPC for AAAI2024, WSDM2024, AAAI2023, KDD2023, WSDM2023, KDD2022, AAAI2022, WSDM2022, AAAI2021, KDD2021

• PC for ICLR2024, NeurIPS 2023, SDM2023, SIGIR2022, ICCV2021, CVPR2021, SIGIR2021, WSDM2021, SDM2021, BigData2021, KDD2020, SIGIR2020, IJCAI2020, AAAI2020, CIKM2020, SDM2020, BigData2020, IJCAI2019, KDD2019, AAAI2019, SDM2019, CIKM2019, BigData2019, KDD2018, AAAI2018, CIKM2018, SDM2018, BigData2018, KDD2017, ICDM2017, SDM2017, CIKM2017, BigData2017, ICOSST-16, IEEE/CIC ICCC'15, AAAI2014, ICWSM2014, ICCC 2014 SNBD, ICWSM2013, IJCAI 2013, IKE 2013, ICDM 2012 etc.

• GIU Outstanding Value Award: significant contribution to enhancing the value of NEC Group and GIU, 2022

• NEC Business Contribution Award: Key Contributions to Automated Security Intelligence as NEC’s Act Secure Security Anomaly Detection Service, 2017

• NEC Excellent Invention Award, 2017: only one invention per year at NEC

• Grand Prize: in Cyber Security Field in Smart Solution Area of MM Research Institute Award, 2017

• SIGKDD Best Research Paper Runner Up Award, 2016

• Grand Prix Award: Automated security intelligence technology at CEATEC, the largest IT and Electronic Show at Asia, 2016

• Travel Awards: NSF-funded Early Career SIAM Travel Award 2013, IJCAI/AIJ Travel Award 2013

• Outstanding Graduate Teaching Assistant Award: North Carolina State University, 2009