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Qiongxiu Li
Qiongxiu Li
Since 2024
Assistant Professor
Department of Electronic Systems
Copenhagen Campus
Aalborg University
Denmark
Research Areas
Research Areas
Secure & Privacy Computation
Signal Processing
Distributed Optimisation
Applications
Applications
Secure Optimization in AI Systems
Contacts
Contacts
qiongxiuli@outlook.com
qili@es.aau.dk
Frederikskaj 12
2450 København SV
Denmark
Research Focus
My research centers on Secure & Privacy Computation, contributing to the theory and practice of trustworthy AI systems. I leverage Signal Processing, Information Theory, Cryptography (e.g., homomorphic encryption, secure multiparty computation), and Distributed Optimisation tools to enable provable privacy guarantees, algorithmic convergence, and communication efficiency. These methods are tailored for modern AI systems (including federated learning, Large-Multimodal-Models, and large-scale models).
PhD Positions
PhD Positions
We are recruiting multiple motivated PhD students to join the AI:SECURITY Lab at Aalborg University (Copenhagen).
Master Thesis & Projects (6-12 months)
Master Thesis & Projects (6-12 months)
My projects are research-driven scientific discovery, if you're enthusiastic about diving into research and pursuing a master's project or thesis within my areas, please send me an email with your cv and research interests.
News
News
[Aug 2025] Congrats to Xiaoyu Luo, "Shared Path: Unraveling Memorization in Multilingual LLMs through Language Similarities" has been accepted to EMNLP 2025. "Demem: Privacy-enhanced Robust Adversarial Learning via De-memorization" has been accepted to MLSP 2025.
[May 2025] Three Papers on privacy-preserving consensus and robust distributed optimization have been accepted to EUSIPCO 2025. Congrats to Wenrui Yu, Zarè Palanciyan (Delft, MSc research project), and Yufei Xia (IP Paris, MSc research project) !
[Jan. 2025] Two papers on LLM and diffusion model security and alignment are accepted in NAACL2025 and ICLR2025.
[Dec. 2024] Two Papers on Distributed Machine Learning and Privacy-Preserving Non-linear Optimization have been accepted to ICASSP 2025. Congrats to Wenrui and Changlong! Preprint links: Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss and Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study
[Nov. 2024] Congratulations to Wenrui Yu on the acceptance of the paper "Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization" in IEEE Transactions on Information Forensics and Security.
[Aug. 2024] I have given a tutorial in EUSIPCO 2024 'Privacy-Preserving Distributed Optimization: Theory, Methods and Applications' together with Prof. Richard Heusdens, Here are the slides.
[Jan. 2024] Our paper on analyzing ML privacy and robustness privacy is accepted in IEEE Transactions on Information Forensics and Security.
[Dec. 2023] Three papers on privacy in distributed optimization are accepted in ICASSP 2024.
[Dec. 2023] Our paper on privacy-preserving distributed average consensus using subspace perturbation is accepted in IEEE Transactions on Information Forensics and Security.
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