Welcome to Zhenlan’s homepage!

Updated on 10 Nov. 2024

Zhenlan Ji is a fourth-year Ph.D. candidate at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, under the supervision of Prof. Shuai Wang. In 2021, he received his Bachelor’s degree in Computer Science and Technology from Nanjing University, Nanjing, China. His research interests include Software Engineering and Deep Learning, with a focus on Causality and LLM-based Agents.

Education

• Ph.D. in Computer Science and Engineering, The Hong Kong University of Science and Technology. Sept 2021 - Now
• B.S. in Computer Science and Technology (FinTech), Nanjing University.
  Sept 2017 - June 2021

Publications

• [ICSE] Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning.
  Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, and Kui Ren.
  In The 46th IEEE/ACM International Conference on Software Engineering, 2024.
  [preprint]

• [ASE] Causality-Aided Trade-off Analysis for Machine Learning Fairness.
  Zhenlan JI, Pingchuan Ma, Shuai Wang, Yanhui Li.
  In The 38th IEEE/ACM International Conference on Automated Software Engineering, 2023.
  [preprint] [code]

• [ASE] PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis.
  Zhenlan JI, Pingchuan Ma, Shuai Wang.
  In The 38th IEEE/ACM International Conference on Automated Software Engineering, 2023.
  [preprint] [code] [documentation]

• [ICSE] CC: Causality-Aware Coverage Criterion for Deep Neural Networks.
  Zhenlan JI, Pingchuan Ma, Yuanyuan Yuan, Shuai Wang.
  In The 45th IEEE/ACM International Conference on Software Engineering, 2023.
  [code]

• [SEKE] Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning.
  Zhenlan JI, Pingchuan Ma, Shuai Wang.
  In The 34th International Conference on Software Engineering and Knowledge Engineering, 2022.
  [code]

• [TIFS] NoLeaks: Differentially Private Causal Discovery Under Functional Causal Model.
  Pingchuan Ma, Zhenlan Ji, Qi Pang, Shuai Wang.
  In IEEE Transactions on Information Forensics and Security, 2022.

Preprint

• [Arxiv] Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs.
  Zhenlan JI, Daoyuan Wu, Pingchuan Ma, Zongjie Li, Shuai Wang.

• [Arxiv] SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner.
  Xunguang Wang, Daoyuan Wu, Zhenlan Ji, Zongjie Li, Pingchuan Ma, Shuai Wang, Yingjiu Li, Yang Liu, Ning Liu, Juergen Rahmel.

• [Arxiv] InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models.
  Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang.

• [Arxiv] Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach.
  Zhenlan JI, Pingchuan Ma, Zongjie Li, Shuai Wang.

Academic Services

Reviewer:

• 2025: ICLR

Shadow Program Committee:

• 2025: ICSE

Artifact Evaluation Committee:

• 2022: ISSTA
• 2023: ISSTA, CCS
• 2024: ICSE

External Reviewer:

• 2022: ASE, AsiaCCS
• 2023: ISSTA, USENIX Security, FSE, CCS, ASE
• 2024: ISSTA, S&P, USENIX Security
• 2025: NDSS, ACL

Publicity Chair:

• 2024: AISTA@ISSRE 2024

Teaching

• Teaching Assistant, COMP2011 - Programming with C++, HKUST, 2022 Fall.
• Teaching Assistant, COMP3633 - Competitive Programming in Cybersecurity II, HKUST, 2023 Spring.
• Teaching Assistant, COMP2633 - Competitive Programming in Cybersecurity I, HKUST, 2024 Fall.