Junxiao Wang (王军晓)
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Postdoctoral Fellow |
About Me
If you're interested in a research internship, we welcome applicants of all levels (PhD/MSc/BSc). Don't hesitate to send me an email!
I currently hold the position of Postdoctoral Fellow at the KAUST Provable Responsible AI and Data Analytics (PRADA) Laboratory, working with Prof. Di Wang.
In 2021-2023, I was a Postdoctoral Fellow at the PolyU Edge Intelligence Laboratory (PEIL) of The Hong Kong Polytechnic University, working with Prof. Song Guo. In 2018-2019, I was a Visiting Student at the Networks Research Group of Queen Mary University of London, working with Prof. Steve Uhlig. I completed my PhD at Dalian University of Technology in 2020, where my mentor was Prof. Keqiu Li. Prior to that, I earned my MEng and BE degrees in 2017, 2014.
Research Interests
I'm broadly interested in machine learning and systems with a special focus on federated learning, trustworthy machine learning and networking.
Recent Professional Activities
Reviewer, for IEEE/CVF International Conference on Computer Vision (ICCV), 2023.05
Postdoctoral Fellow, working with Prof. Di Wang, at Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), 2023.02
Invited Talk, in Ritsumeikan University and CCF Dalian International Seminar, 2022.03
Postdoctoral Fellow, working with Prof. Song Guo, at Department of Computing (COMP), The Hong Kong Polytechnic University (PolyU), 2021.03-2023.02
Session Chair, for IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2019.12
Visiting Student, working with Prof. Steve Uhlig, at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), 2018.10-2019.09
News
May 2023 Our paper entitled "Investigating Trojan Attacks on Pre-trained Language Model-powered Database Middleware" was accepted by KDD2023.
Mar 2023 Our paper entitled "DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning" was available in arxiv.org.
Feb 2023 Our paper entitled "PMR: Prototypical Modal Rebalance for Multimodal Learning" was accepted by CVPR2023.
Jan 2023 Our paper entitled "pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning" was accepted by WWW2023.
Nov 2022 Our paper entitled "Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application" was available in arxiv.org.
Nov 2022 Our paper entitled "PMR: Prototypical Modal Rebalance for Multimodal Learning" was available in arxiv.org.
Nov 2022 Our paper entitled "FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers" was available in arxiv.org.
Aug 2022 Our paper entitled "PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models — Federated Learning in Age of Foundation Model" was available in arxiv.org.
Aug 2022 Our paper entitled "Federated Unlearning: Guarantee the Right of Clients to Forget" was accepted by IEEE Network.
Apr 2022 Our paper entitled "Efficient Integrity Authentication Scheme for Large-scale RFID Systems" was accepted by IEEE Transactions on Mobile Computing.
Apr 2022 Our paper entitled "A Survey on Gradient Inversion: Attacks, Defenses and Future Directions" was accepted by IJCAI2022.
Feb 2022 Our paper entitled "Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space" was available in arxiv.org.
Jan 2022 Our paper entitled "Federated Unlearning via Class-Discriminative Pruning" was accepted by WWW2022.
Dec 2021 Our paper entitled "Protect Privacy from Gradient Leakage Attack in Federated Learning" was accepted by INFOCOM2022.
Oct 2021 Our paper entitled "Federated Unlearning via Class-Discriminative Pruning" was available in arxiv.org.
Feb 2020 Our paper entitled "Click-UP: Toward the Software Upgrade of Click-Based Modular Network Function" was accepted by IEEE Systems Journal.
Jun 2018 Our paper entitled "CLICK-UP: Towards Software Upgrades of Click-driven Stateful Network Elements" was accepted by Demo track of SIGCOMM2018.