Zhengzhang Chen

Zhengzhang Chen

Senior Research Scentist at NEC Laboratories America

Biography

Zhengzhang Chen is a Senior Research Scientist at NEC Laboratories America. Previously, he was a Research Assistant Professor in the Department of Electrical Engineering & Computer Science at Northwestern University, where he also completed a one-year postdoctoral fellowship under the supervision of Prof. Alok Choudhary. He received his Ph.D. in Computer Science from North Carolina State University.

His research focuses on advancing real-world intelligent systems with improved security, robustness, and decision-making capabilities for complex, dynamic environments. His primary interests include graph AI, anomaly detection, causal AI, AI security, and LLM agents & reasoning, with applications spanning computer security, IoT/OT systems, climate science, social media analytics, materials science, bioinformatics, and finance.

He has published over 80 peer-reviewed papers in major academic venues such as CVPR, NeurIPS, ICLR, ACL, NAACL, KDD, AAAI, WWW, and IJCAI. His research has been featured by NSF News, DOE ASCR Discovery, NHK TV News, The Conversation, and esciencenews. His research has also led to over 100 patent filings, 48 of which have been granted. Additionally, he has served as an Area Chair or Senior Program Committee member for major conferences including NeurIPS, SIGKDD, AAAI, IJCAI, WSDM, and CIKM.

News

Selected Publications [See More]

# indicates corresponding author

LLM Agents & Reasoning

  • Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation. Minhua Lin, Zhengzhang Chen#, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, and Haifeng Chen. EACL 2026.
  • SolverLLM: Leveraging Test-Time Scaling for Optimization Problems via LLM-Guided Search. Dong Li, Xujiang Zhao, Linlin Yu, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Zhong Chen, Feng Chen, Chen Zhao, and Haifeng Chen. NeurIPS 2025.
  • Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery. ChengAo Shen, Zhengzhang Chen#, Dongsheng Luo, Dongkuan Xu, Haifeng Chen, and Jingchao Ni. ACL 2025 (Findings).
  • MixLLM: Dynamic Routing in Mixed Large Language Models. Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, and Haifeng Chen. NAACL 2025.
  • Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey. C. Ling, Zhengzhang Chen, et al. ACM Computing Surveys 2025.

    • 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.
  • 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

  • Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion. Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen, and Jundong Li. ICML 2025.
  • 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.