Hui Wang

Security and privacy in machine learning
Trustworthy machine learning
Information security

Dr. Wendy Hui Wang is a full professor of the Computer Science Department of the Stevens Institute of Technology. She received her Ph.D. degree in Computer Science from the University of British Columbia, Vancouver, Canada. She has been awarded numerous federal research grants and contracts, primarily as Principal Investigator. Dr. Wang received the NSF CAREER Award in 2014 for her research on verifiable computing and machine learning.

Stevens Institute of Technology
2022 - Present, Professor
2016 - 2022, Associate Professor
2008 - 2016, Assistant Professor

2022 - Present, Professor, Stevens Institute of Technology

2016 - 2022, Associate Professor, Stevens Institute of Technology

2008 - 2016, Assistant Professor, Stevens Institute of Technology

Honorable mention for the Best Student Paper Award. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2025.

IEEE Big Data Security Woman of Achievement Award, 2025.

Best Paper Award. The Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, affiliated with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019

Best Paper Award. IEEE International Conference on Information Reuse and Integration (IRI), 2017.

CAREER award, National Science Foundation, 2014.

National Science Foundation, Secure and Trustworthy Cyberspace (SaTC) program,\emph{SaTC: CORE: Small: Securing Network Embedding against Privacy Attacks}, 01/2022 - 12/2025, Single PI.

Cisco, Security Compliance Verification, Stevens Institute of Technology, 01/01/2022 - Present.

National Science Foundation, Secure and Trustworthy Cyberspace (SaTC) program, SaTC: CORE: Medium: Privacy for All: Ensuring Fair Privacy Protection in Machine Learning, PI, 01/2021 - 12/2025.

National Science Foundation, Secure and Trustworthy Cyberspace (SaTC) program,\emph{SaTC-EDU: EAGER: Development and Evaluation of Privacy Education Tools via Open Collaboration}, PI, 06/2015 - 05/2017.

National Science Foundation, Faculty Early Career Development (CAREER) program, CAREER: Verifiable Outsourcing of Data Mining Computations, 06/2014-05/2021.

[CSUR'26] Jie Fu, Yuan Hong, Xinpeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen and Yang Cao, Differentially Private Federated Learning: A Systematic Review, ACM Computing Surveys (CSUR), Accepted, 2026. Impact Factor: 28.0.

[CCS'25] Jie Fu, Yuan Hong, Zhili Chen, Wendy Hui Wang. Safeguarding Graph Neural Networks against Topology Inference Attacks. The ACM Conference on Computer and Communications Security (CCS). Oct 13- 17, 2025. Taipei, Taiwan.

[ECML/PKDD'25 Workshop] Nan Cui, Wendy Hui Wang, Yue Ning. Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters. Workshop on Bias and Fairness in AI affiliated with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Sept 15 - 19, 2025. Porto, Portugal.

[Usenix Security'25] Nima Naderloui, Shenao Yao, Binghui Wang, Jie Fu, Wendy Hui Wang, Weiran Liu, Yuan Hong. Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective. The USENIX Security Symposium. August 13 - 15, 2025. Seattle, WA.

[SIGKDD'25] Kun Wu, Wendy Hui Wang. Verification of Incomplete Graph Unlearning through Adversarial Perturbations. The ACM SIGKDD Conference on Knowledge Discovery and Data Mining. August 3 - 7, 2025, Toronto, Canada (Acceptance rate 18.4%). Honorable mention for the Best Student Paper Award.

[CIKM'24] Da Zhong, Xiuling Wang, Zhichao Xu, Jun Xu, Wendy Hui Wang. Interaction-Level Membership Inference Attack against Recommender Systems with Long-Tailed Distribution. The Conference on Information and Knowledge Management (CIKM), Oct 21-25, 2024, Boise, Idaho, USA.

[PETS'24] Xiuling Wang, Wendy Hui Wang. Subgraph Structure Membership Inference Attacks against Graph Neural Networks. The 24th Privacy Enhancing Technologies Symposium (PETS), July 15–20, 2024, Bristol, UK.

[PETS'24] Xiuling Wang, Wendy Hui Wang. GCL-Leak: Link Membership Inference Attacks against Graph Contrastive Learning. The 24th Privacy Enhancing Technologies Symposium (PETS), July 15–20, 2024, Bristol, UK.

[S&P'24] Ruikai Zhou, Kang Yang, Xiuling Wang, Wendy Hui Wang, Jun Xu. Revisiting Black-box Ownership Verification for Graph Neural Networks. IEEE Symposium on Security and Privacy (S&P). May 20 - 23, 2024. San Francisco, CA.

[AAAI'24] Junjie Chen, Jiahao Li, Song Chen, Bin Li, Qingcai Chen, Hongchang Gao, Hui Wang, Xuzeng Lin, Mingdy Shi. Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks. The 38th AAAI Conference on Artificial Intelligence (AAAI). February 20 - 27, 2024. Vancouver, Canada.

[ICDM'24] Nan Cui, Xiuling Wang, Wendy Hui Wang, Violet Chen, Yue Ning. Equipping Federated Graph Neural Networks with Structure-aware Group Fairness. IEEE International Conference on Data Mining (ICDM). December 1-4, 2023. Shanghai, China.

[ACSAC'23] Xiuling Wang, Wendy Hui Wang. Link Membership Inference Attacks against Unsupervised Graph Representation Learning. The Annual Computer Security Applications Conference (ACSAC). December 4 - 8, 2023. Austin, Texas.

[SIGKDD'23] Kun Wu, Jie Shen, Yue Ning, Ting Wang, Wendy Hui Wang. Certified Edge Unlearning for Graph Neural Networks. 2023 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), August 6 - 10, 2023. Long Beach, CA.

[PETS'23] Da Zhong, Ruotong Yu, Kun Wu, Xiuling Wang, Jun Xu, Wendy Hui Wang. Disparate Vulnerability in Link Inference Attacks against Graph Neural Networks. The 23rd Privacy Enhancing Technologies Symposium (PETS), July 10–15, 2023, Lausanne, Switzerland.

CS442 (Database Management System), Stevens Institute of Technology

CS609 (Data Management and Exploration on the Web), Stevens Institute of Technology