I am a final-year Ph.D. candidate in Electrical Engineering and Computer Science (EECS) at the University of Kansas, advised by Prof. Fengjun Li and Prof. Bo Luo. I expect to graduate in December 2026 and am actively seeking full-time opportunities beginning in 2027. My research focuses on the security and privacy of machine learning systems, including model protection, deepfake defense, and image copyright protection. I am also broadly interested in machine learning systems and applications, particularly recommender systems and large language models.
Education
- Ph.D. in Computer Science, University of Kansas (Aug 2021 – Dec 2026 (Expected))
- M.Eng. in Computer Technology, University of Chinese Academy of Sciences (Sep 2017 – Jul 2020)
- B.Eng. in Network Engineering, Shandong University of Science and Technology (Sep 2013 – Jun 2017)
Experience
- Applied Scientist Intern, Amazon Prime Video, Seattle, WA (May 2025 – Aug 2025)
- Developed an end-to-end LLM-based session-aware recommendation pipeline that generated short-term viewer profiles and re-ranked retrieved video candidates based on inferred viewer intent.
- Applied Scientist Intern, Amazon Prime Video, Seattle, WA (Jun 2024 – Sep 2024)
- Developed an LLM-based emotion-aware recommendation pipeline that extracted emotion signatures from large-scale user reviews and integrated them into semantic video representations for retrieval and ranking.
- Software Development Engineer Intern, Baidu, Beijing (Jan 2021 – May 2021)
- Developed a cross-compiled SDK for ARM-based translation devices by re-wrapping Baidu Translation’s x86 SDK functionality and encapsulating online API calls to Baidu Translation services.
Publications
- PhantomSeal: Proactive Deepfakes Defense with Identity/Context Protection and Forensic Tracing
CCS 2026 [ArXiv, Code] Liangqin Ren, Zeyan Liu, Ye Wang, Yuxin Chen, Fengjun Li, and Bo Luo - Enforcing cryptographic distributed-VCS access control with no trust on servers
JISA 2025 [ArXiv] Xin Xu, Zhen Yang, Quanwei Cai, Jingqiang Lin, Liangqin Ren, Bo Chen, and Yongfeng Huang - The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
ESORICS 2024 [ArXiv] Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, and Bo Luo - PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption
PETS 2024 [ArXiv, Code] Liangqin Ren, Zeyan Liu, Fengjun Li, Kaitai Liang, Zhu Li, and Bo Luo
Services
- Reviewer for TDSC (2025-2026) and ISCAS 2025.
- Session Moderator for SecureComm 2022.
Teaching
- Teaching Assistant, EECS 556 Introduction to Information and Computer Security, University of Kansas (Fall 2026)
- Teaching Assistant, EECS 348/448 Software Engineering, University of Kansas (Fall 2022 – Spring 2026)