Luke Yukun Jiang (江钰坤 in Chinese)

Biography

I am Luke, a Ph.D. student at CISPA Helmholtz Center for Information Security (Saarbrücken, Germany), co-supervised by Prof. Michael Backes and Dr. Yang Zhang. I obtained my B.E. degree at Sichuan University (Chengdu, China) advised by Prof. Beibei Li in 2022.

Research Interests

• AI Security
• Privacy Preservation

News

• Sep. 2024: our work ‘‘ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities’’ has been accepted by EMNLP 2024. See you in Miami.
• May 2024: I have successfully passed the qualifying exam (QE).
• Apr. 2024: our work ‘‘Games and Beyond: Analyzing the Bullet Chats of Esports Livestreaming’’ has been accepted by ICWSM 2024. See you in Buffalo.

Research & Working Experience

• Nov. 2022 - Present, Ph.D. student at CISPA, advised by Prof. Michael Backes and Dr. Yang Zhang
• Feb. 2022 - Aug. 2022, Research Intern at Tencent Cloud, advised by Dr. Yong Cheng
• Apr. 2020 - June 2022, Research Assistant at Sichuan University, advised by Prof. Beibei Li
• Jul. 2021 - Nov. 2021, Remote Research Intern at Duke Univesity, advised by Prof. Neil Gong

Publications

• Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, Yang Zhang: ‘‘ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities.’’ The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) [PDF] [Link] [Code]
• Yukun Jiang, Xinyue Shen, Rui Wen, Zeyang Sha, Junjie Chu, Yugeng Liu, Michael Backes, Yang Zhang: ‘‘Games and Beyond: Analyzing the Bullet Chats of Esports Livestreaming.’’ International AAAI Conference on Web and Social Media (ICWSM 2024) [PDF] [Link]
• Yukun Jiang, Xiaoyu Cao, Hao Chen, Neil Gong: ‘‘FedER: Communication-Efficient Byzantine-Robust Federated Learning.’’ In International Conference on Learning Representations 2022 Workshop on Socially Responsible Machine Learning (ICLR 2022-SRML) [PDF]
• Beibei Li, Yukun Jiang, Qingqi Pei, Tao Li, Liang Liu, Rongxing Lu: ‘‘FEEL: Federated End-to-End Learning with Non-IID Data for Vehicular Ad Hoc Networks.’’ In IEEE Transactions on Intelligent Transportation Systems T-ITS (T-ITS) [Link]
• Beibei Li, Yukun Jiang, Wenbin Sun, Weina Niu, Peiran Wang: ‘‘FedVANET: Efficient Federated Learning with Non-IID Data for Vehicular Ad Hoc Networks.’’ In Proceedings of IEEE Global Communications Conference 2021 (GLOBECOM 2021) [Link] [PDF]
• Beibei Li, Yaxin Shi, Yuqing Guo, Qinglei Kong, Yukun Jiang: ‘‘Incentive-Based Adaptive Federated Knowledge Distillation for Cross-Silo Applications.’’ In Proceedings of IEEE International Conference on Computer Communications Workshops (INFOCOM 2022 WORKSHOPS) [PDF]
• Beibei Li, Peiran Wang, Hanyuan Huang, Shang Ma, Yukun Jiang: ‘‘FLPhish: Reputation-Based Phishing Byzantine Defense in Ensemble Federated Learning.’’ In Proceedings of IEEE Symposium on Computers and Communications 2021 (ISCC 2021) Best Paper Award [Link] [PDF]

Awards

• Best Paper Award, ISCC 2021

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