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Jie Lin

Lecturer

BIOGRAPHY

Jie Lin is a lecturer in the Department of Computer Science at the University of Central Florida. He recently earned his doctoral degree in computer science from UCF, with research emphasizing security, privacy, vulnerability analysis and applications of large language models and deep learning. Lin’s scholarly contributions focus on empirical evaluations and practical strategies to enhance AI-driven security systems.

Before joining UCF as a lecturer, Lin held roles as a full-stack software developer and manager, overseeing various business operations ranging from small merchandise trading to real estate investments. Additionally, he served as a graduate teaching assistant at UCF, employing hands-on, innovative teaching practices. He holds a master’s degree in computer science from Emory University and a bachelor’s degree in computer science and mathematics from Ursinus College. Lin is passionate about creating collaborative, engaging educational environments that emphasize critical thinking and practical problem-solving skills to equip students for success in computer science and cybersecurity fields.

EDUCATION

  • Ph.D. in Computer Science, University of Central Florida
  • M.S. in Computer Science, Emory University
  • B.S. in Computer Science and Mathematics, Ursinus College

RESEARCH

  • Security and robustness of Large Language Models (LLMs)
  • Vulnerability detection and localization in source code
  • Context window optimization
  • Hallucination mitigation

PUBLICATIONS

    • Jie Lin and David Mohaisen, “From Large to Mammoth: A Comparative Evaluation of Large Language Models in Vulnerability Detection,” NDSS, 2025. (San Diego, CA, Feb. 24–28)
    • Jie Lin and David Mohaisen, “Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows,” The 23rd International Conference on Machine Learning and Applications (ICMLA), 2024. (Miami, FL, Dec. 18–22)
    • Mohamed Alqadhi, Mohammed Alkinoon, Jie Lin, Ahmed Abdalaal, and David Mohaisen, “Entangled Clouds: Measuring the Hosting Infrastructure of the Free Contents Web,” CCSW, 2023. (In conjunction with ACM CCS, Copenhagen, Denmark, Nov. 26)
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