Zhengzhang Chen

Zhengzhang Chen

Senior Research Scentist at NEC Laboratories America

About Me

I am currently a senior research scientist at NEC Laboratories America. Previously, I served as a research assistant professor in the Department of Electrical Engineering & Computer Science at Northwestern University, where I also completed a one-year postdoctoral fellowship under the supervision of Prof. Alok Choudhary. I received my Ph.D. degree from the Computer Science Department at North Carolina State University.

My research aims to advance real-world applications and systems through enhanced intelligence, increased security, and more effective decision-making capabilities. I have been primarily working on graph mining, anomaly detection, causal AI, AI security, graph neural networks, text mining, and their applications in computer security, IoT/OT systems, climate science, social media analytics, material science, bioinformatics, and finance, enabling machines to better understand real-world complex dynamic systems.

I have published over 80 peer-reviewed research papers in major academic venues such as CVPR, NeurIPS, KDD, ICLR, AAAI, WWW, and IJCAI. My work has been featured in various news media including NSF News, DOE ASCR Discovery, NHK TV News, The Conversation, and esciencenews. Additionally, my research has led to the filing of over 100 patents, with 40 already issued.

News

Selected Publications [See More]

Causal Structure Learning & Root Cause Analysis & AIOps

  • MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems. Lecheng Zheng, Zhengzhang Chen, Jingrui He, and Haifeng Chen. WWW 2024.
  • Interdependent Causal Networks for Root Cause Localization. Dongjie Wang*, Zhengzhang Chen*, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen. SIGKDD 2023.
  • Incremental Causal Graph Learning for Online Root Cause Analysis. Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, and Haifeng Chen. SIGKDD 2023.
  • Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection. Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, and Jiliang Tang. SIGKDD 2021.
  • LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis. Lecheng Zheng, Zhengzhang Chen, Dongjie Wang, Chengyuan Deng, Reon Matsuoka, and Haifeng Chen. Arxiv 2024.

    • AI for Security & Security for AI

  • Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning. Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu. TNNLS 2022.
  • FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems. Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, and Yevgeniy Vorobeychik. CVPR 2021.
  • Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection. Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, and Jiliang Tang. SIGKDD 2021.
  • Heterogeneous Graph Matching Networks for Unknown Malware Detection. Shen Wang*, Zhengzhang Chen*, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, and Philip S. Yu. IJCAI 2019.
  • NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage. Wajih Ul Hassan, Shengjian Guo, Ding Li, Zhengzhang Chen, Kangkook Jee, Zhichun Li, and Adam Bates. NDSS 2019.

    • Meta Learning & Domain Adaptation & Transfer Learning

  • POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning. Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, and Haifeng Chen. SIGKDD 2024.
  • Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning. Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, and Yan Liu. NeurIPS 2023.
  • Towards Learning Disentangled Representations for Time Series. Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Jingchao Ni, Denghui Zhang, Haifeng Chen, and Xia Hu. SIGKDD 2022.
  • Superclass-Conditional Gaussian Mixture Model for Learning Fine-Grained Embeddings]. Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, and Haifeng Chen. ICLR 2022.
  • TINET: Learning Invariant Networks via Knowledge Transfer. Chen Luo*, Zhengzhang Chen*, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, and Jieping Ye. SIGKDD 2018.