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Zhen Zeng

Zhen Zeng

  • Assistant Professor, Computer Science

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Dr. Zeng’s general research interest lies in secure compound AI systems, with an emphasis on integrating trustworthiness and resilience into the AI service lifecycle, spanning development, deployment, and operation, to deliver high performance in cloud, edge, and on-premises environments. She leads the Secure and Intelligent Distributed Computing Lab (SIDC Lab) at UWM. Her current research focuses on the intersection of applied AI/ML, compound AI system, edge intelligence, and security, particularly in the context of AI-powered cyber-physical systems (CPS). With an equal commitment to cybersecurity and AI education, she works to empower both professionals and the broader workforce with the skills needed for the AI era.

She has served on the federal grant review panel, the editorial board of the IEEE Journal, the technical program committee and the organization committee of the IEEE/ACM conferences, and the IEEE/ACM Journal reviewer on cybersecurity, networking, reliability, and cloud computing. Before joining UWM in 2023, she gained valuable industry experience in the high-tech sector, focusing on power management and cloud native technologies. She also collaborates extensively with industry to enhance AI adoption and innovation in manufacturing.

Education

  • PhD, Arizona State University, 2022
  • MS, Purdue University, 2014
  • BS, Xidian University, 2005

Research Interests

  • Applied AI/ML
  • AI System Security
  • Edge Intelligence & Security
  • AI and Cybersecurity Education

Community Involvement

  • IEEE Senior Member; ACM Member

  • Panelist, NSF Proposal Review Panels, 2024, 2025

  • Associate Editor, IEEE Transactions on Network and Service Management (2025- )

  • Organizing Committee
    • Student Travel Grant Chair, IEEE International Conference on Computer Communications (INFOCOM 2025, INFOCOM 2026)
    • Social Media Chair, IEEE International Conference on Cloud Engineering (IC2E 2024)
  • Journal Reviewer: IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Network and Service Management (TNSM), IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Reliability, ACM Transactions on Cyber-Physical Systems, IEEE Transactions on Education (ToE)

  • Technical Program Committee, CCS 2024 workshop on Adaptive and Autonomous Cyber Defense, NDSS 2024 workshop on AI System with Confidential Computing , CCS 2023 workshop on Moving Target Defense

  • Faculty Mentor, Wisconsin Louis Stokes Alliance for Minority Participation (WiscAMP) STEM-Inspire Program since 2023

Selected Honors & Awards

  • "The Teacher Excellence Award" at UWM, 2024 Spring

Selected Publications

  • Zhang, Z., Wu, L., Zeng, Z., and Gu, Z., 2025. Saliency-Guided Lightweight Backdoor Defense for Edge Intelligence. In The Tenth ACM/IEEE Symposium on Edge Computing (SEC’25), Accepted.
  • Wu, L., Zeng, Z., Gu, Z., and Wu, P., 2025. How Heavy is the Edge? Resource Utilization of Edge Generative AI on Distributed AI Infrastructure. In The Tenth ACM/IEEE Symposium on Edge Computing (SEC ’25), Accepted.
  • Taveira,G., Zhang, Z., Zeng, Z., and Gu, Z., 2025. Poster: A Lightweight Pruning for Mitigating Neural Network Backdoor on Edge. In The Tenth ACM/IEEE Symposium on Edge Computing (SEC ’25), Accepted.
  • Van Bossuyt, D. L., Allaire, D., Bickford, J. F., Bozada,... & Zeng, Z., 2025. The Future of Digital Twin Research and Development. Journal of Computing and Information Science in Engineering, 25(8).
  • Zhang, Z., Elsharef, I., Zeng, Z., 2024. Unveiling Neural Network Data Free Backdoor Threats in Industrial Control Systems, In Proceedings of the 2024 ACM Computer and Communications Security Workshop on Re-design Industrial Control Systems with Security, Oct. 2024.
  • Zeng, Z., Chung, C., Xie, L., 2024. The Development of A Large-Scale Cloud Emulator, In 2024 IEEE International Conference on Cloud Engineering (IC2E), Sept. 2024.
  • Elsharef, I., Zeng, Z., Gu, Z, 2024. Facilitating Threat Modeling by Leveraging Large Language Models, The Network and Distributed System Security Symposium workshop on AI System with Confidential Computing 2024.
  • Zeng, Z., Huang, D., Xue, G., Deng, Y., Vadnere, N. and Xie, L., 2023. ILLATION: Improving Vulnerability Risk Prioritization By Learning From Network. IEEE Transactions on Dependable and Secure Computing (TDSC).
  • Zeng, Z., Yang, Z., Huang, D., & Chung, C. J. 2021. LICALITY—Likelihood and Criticality: Vulnerability Risk Prioritization Through Logical Reasoning and Deep Learning. IEEE Transactions on Network and Service Management (TNSM).
  • Deng, Y., Zeng, Z., & Huang, D. 2021, June. Neocyberkg: Enhancing cybersecurity laboratories with a machine learning-enabled knowledge graph. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE).
  • Zeng, Z., Deng, Y., Hsiao, I., Huang, D., & Chung, C. J. 2018, October. Improving student learning performance in a virtual hands-on lab system in cybersecurity education. In 2018 IEEE Frontiers in Education Conference (FIE).
  • Zeng, Z., Deng, Y., Hsiao, S., Huang, D., & Chung, C. J. 2018, February. Conceptualizing Student Engagement in Virtual Hands-on Lab: Preliminary Findings from a Computer Network Security Course. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE).