This is Lixu Wang. I am now a research fellow at Nanyang Technological University (NTU) and working with Prof. Xiaofeng Wang and Prof. Wei Dong. I received my Ph.D. in Computer Science at Northwestern University (NU), advised by Prof. Qi Zhu and Prof. Xiao Wang, and supported by IBM PhD Fellowship. Prior to Northwestern, I obtained my B.E. from Zhejiang University (ZJU) in July 2020 under the supervision of Prof. Wenyuan Xu. I am open to all kinds of collaboration, please email me if you are interested in my research.

My research is dedicated to building high-quality AI models from data that carry various real-world concerns in quality, privacy, and copyright. Addressing these foundational data challenges unlocks significant benefits. By enabling AI to learn effectively from broader, more diverse datasets at lower cost, we empower the creation of robust models capable of transforming multiple domains. At the same time, policymakers worldwide are raising the bar for data governance. AI systems built with these data concerns addressed can support lawful data use, protect data owners’ rights, and maintain compliance across their full lifecycle. To this end, I develop novel methods in federated learning, outsourced computation, machine unlearning, and secure inference and training, drawing on optimization theory, information theory, and cryptography.

🔥 News

• 2025.07:   🎉Our proposal about privacy-preserving data transaction has been successfully funded (210,000 $SGD).
• 2025.06:   🎉Our papers about dynamic FL, FL prompt tuning, and visual privacy leakage have been accepted by ICCV 2025 and MM 2025.
• 2025.02:   🎉Our paper about private downstream task adaptation of pre-trained transformers has been accepted to CVPR 2025.
• 2025.01:   🎉Our papers about LLM unlearning and backdoor attacks have been accepted by ICLR 2025.
• 2024.09:   🎉Our paper about Image Retrieval has been accepted by NeurIPS 2024.
• 2024.03:   Help to submit one NSF proposal with CISPA.
• 2023.12:   🎉Our paper about anomaly detection has been accepted by ICASSP 2024.
• 2023.09:   🎉Our paper about data IP has been accepted by NeurIPS 2023.
• 2023.08:   🎉Super excited to be an IBM PhD Fellowship recipient.
• 2023.07:   🎉Our paper about backdoor detection has been accepted by ICCV 2023.
• 2023.04:   🎉Help submit two NSF proposals accepted later.
• 2023.01:   🎉Our paper about domain generalization has been accepted by ICLR 2023.
• 2022.11:   Become a visiting research scientist at General Motors.
• 2022.03:   🎉Our paper about incremental learning is accepted by CVPR 2022.
• 2022.02:   🎉Our paper about incremental learning is accepted by TNNLS 2021.
• 2022.01:   🎉Our paper about model IP is accepted by ICLR 2022 as an oral presentation.
• 2021.07:   🎉Our paper about domain adaptation is accepted by ICCV 2021.
• 2021.03:   🎉Our paper about digital watermarks is accepted by USENIX Security 2021.
• 2020.12:   🎉Our paper about federated learning is accepted by AAAI 2021.
• 2020.07:   🎉Received Outstanding Undergraduate Dissertation Award of ZJU.

📝 Publications

* denotes equal contribution, # denotes corresponding authors

Year 2025

• Federated Continuous Category Discovery and Learning
  • Lixu Wang*, Chenxi Liu*, Junfeng Guo, Qingqing Ye, Heng Huang, Haibo Hu, Wei Dong
  • IEEE/CVF Conference on Computer Vision, ICCV 2025
• Split Adaptation for Pre-trained Vision Transformers
  • Lixu Wang*, Bingqi Shang*, Yi Li, Payal Mohapatra, Wei Dong, Xiao Wang, Qi Zhu
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025
• On Large Language Model Continual Unlearning
  • Lixu Wang* #, Chongyang Gao*, Kaize Ding, Chenkai Weng, Xiao Wang, Qi Zhu
  • International Conference on Learning Representation, ICLR 2025
• FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models
  • Jiaqi Wu, Simin Chen, Jing Tang, Yuzhe YANG, Yiming Chen, Lixu Wang, Song Lin, Zehua Wang, Wei Chen, Zijian Tian
  • IEEE/CVF Conference on Computer Vision, ICCV 2025
• Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems
  • Ruochen Jiao, Shaoyuan Xie, Justin Yue, Takami Sato, Lixu Wang, Yixuan Wang, Qi Alfred Chen, Qi Zhu
  • International Conference on Learning Representation, ICLR 2025
• The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework
  • Feiran Liu, Yuzhe Zhang, Xinyi Huang, Yinan Peng, Xinfeng Li, Lixu Wang, Yutong Shen, Ranjie Duan, Simeng Qin, Xiaojun Jia, Qingsong Wen, Wei Dong
  • ACM International Conference on Multimedia, MM 2025
• Shallow Flow Matching for Coarse-to-Fine Text-to-Speech Synthesis
  • Dong Yang, Yiyi Cai, Yuki Saito, Lixu Wang, Hiroshi Saruwatari
  • Arxiv 2025

Year 2024

• Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
  • Lixu Wang, Xinyu Du, Qi Zhu
  • 38th Conference on Neural Information Processing Systems, NeurIPS 2024
• DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
  • Lixu Wang, Shichao Xu, Xinyu Du, Qi Zhu
  • International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
• A Survey of Federated Unlearning: A Taxonomy, Challenges, and Future Directions
  • Jiaxi Yang, Yang Zhao, Yiling Tao, Lixu Wang, Xiaoxiao Li, Dusit Niyato
  • IEEE Network Magazine 2024
• Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
  • Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu
  • Arxiv 2024
• Phase-driven Generalization for Nonstationary Time Series
  • Payal Mohapatra*, Lixu Wang*, Qi Zhu
  • Under revision of TMLR
• Learning From Each Other: Generalized Federated Incremental Semantic Segmentation
  • Jiahua Dong, Wenqi Liang, Yang Cong, Gan Sun, Lixu Wang, Henghui Ding, Yulun Zhang, Luc Van Gool
  • Under revision of TPAMI
• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
  • Jiaqi Wu, Shihao Zhang, Simin Chen, Lixu Wang, Zehua Wang, Wei Chen, Fangyuan He, Zijian Tian, F Richard Yu, Victor Leung
  • Under revision of TMC

Year 2023

• Deja Vu: Continual Model Generalization for Unseen Domains
  • Lixu Wang* #, Chenxi Liu*, Linjun Lu, Chen Sun, Xiao Wang, Qi Zhu
  • International Conference on Learning Representation, ICLR 2023
• PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning
  • Junfeng Guo, Ang Li, Lixu Wang, Cong Liu
  • IEEE/CVF Conference on Computer Vision, ICCV 2023
• Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
  • Junfeng Guo, Yiming Li, Lixu Wang, Shu-Tao Xia, Heng Huang, Cong Liu, Bo Li
  • 37th Conference on Neural Information Processing Systems, NeurIPS 2023

Year 2022

• Federated Class-Incremental Learning
  • Lixu Wang*, Jiahua Dong*, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
• Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
  • Lixu Wang*, Shichao Xu*, Ruiqi Xu, Xiao Wang, Qi Zhu
  • International Conference on Learning Representation, ICLR 2022 Oral (top 1.5%)
• Incremental 3D Object Recognition for Point Cloud Representation
  • Jiahua Dong, Gan Sun, Lixu Wang, Jun Li, Lingjuan Lyu, Konukoglu Ender
  • IEEE Transaction on Neural Networks and Learning Systems, TNNLS 2022

Year 2021 and before

• Weak Adaptation Learning: Addressing Cross-domain Data Insufficiency with Weak Annotator
  • Shichao Xu*, Lixu Wang*, Yixuan Wang, Qi Zhu
  • IEEE/CVF Conference on Computer Vision, ICCV 2021
• Addressing Class Imbalance in Federated Learning
  • Lixu Wang, Shichao Xu, Xiao Wang, Qi Zhu
  • AAAI Conference on Artificial Intelligence, AAAI 2021
• mID: Tracing Screen Photos via Moire Patterns
  • Yushi Cheng, Xiaoyu Ji, Lixu Wang*, Qi Pang*, Yi-Chao Chen, Wenyuan Xu
  • USENIX Security Symposium, Usenix Security 2021
• Eavesdrop the Composition Proportion of Training Labels in Federated Learning
  • Lixu Wang, Shichao Xu, Xiao Wang, Qi Zhu
  • Arxiv 2019

🎖 Honors and Awards

• 2023 - 2024: IBM Ph.D. Fellowship
• 2020: Outstanding Undergraduate Dissertation Award (ZJU)
• 2020: Computer Science Ph.D. Fellowship (NU)
• 2020: Outstanding Graduate of Zhejiang (Zhejiang Provincial Government)
• 2019: Wang Guosong Outstanding Scholarship (ZJU highest honor in EECS)
• 2018: 1st Prize, Chinese Mathematical Contest in Modeling, top 0.3%

📖 Educations

• 2021.01 - 2024.12, Northwestern University, Ph.D. in Computer Science, GPA: 4.0/4.0
• 2016.09 - 2020.07, Zhejiang University, B.E. in Electrical and Computer Engineering, GPA: 3.96/4.0

💬 Invited Talks

• 2022.07, Contrastive Learning for Time-Series Anomaly Detection, General Motors invited talks.
• 2022.06, Machine Learning with Constraints from Generalization, ZJU invited talks, Hangzhou.
• 2022.05, Non-Transferable Learning for Model IP Protection, Sony AI Journal Club.
• 2022.03, Federated Class-Incremental Learning, SSFAI Academic Invited Talks.

💻 Services

• Reviewer/Program Committee: NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, AAAI, MM, WACV, TNNLS, TCSVT, etc.
• Advising: Ruiqi Xu (B.S. NU → Ph.D. Uchicago); Chenxi Liu (M.S. NU → Ph.D. UMaryland), Jinjin Cai (M.S. NU → Ph.D. Purdue), Yufei Wang (M.S. NU → Ph.D. Northeastern), Shiyuan Duan (M.S. NU → Ph.D. UIUC), Bingqi Shang (M.S. NU → Ph.D. MSU).