Jingwen Shi β˜•οΈ
Jingwen Shi

Ph.D. Candidate

About Me

Jingwen is a Ph.D. candidate in the Computer Science and Engineering Department at Michigan State University, working under the supervision of Prof.Guan-Hua Tu. She earned her Master’s degree from the University of Chinese Academy of Sciences, where she worked with Prof.Chengzhong Xu and Prof.KeJiang Ye. Before that, she received her Bachelor’s degree from Hunan University, under the guidance of Prof.Sheng Xiao. Her research interests span Mobile Systems & Networks, Security, AI+Systems and Distributed Systems, with a recent focus on securing and advancing 5G IoT/IoV and O-RAN technologies.

Email: shijingw at msu dot edu

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Interests
  • Mobile Systems & Networks
  • Security
  • Internet of Things (IoT)
  • Internet of Vehicles (IoV)
  • Distributed System
  • AI for System
Education
  • Ph.D. Computer Science

    Michigan State University

  • MSc Applied Computer Technology

    University of Chinese Academy of Sciences

  • BEng Information Security

    Hunan University

πŸ“š My Research

Hi there,

I am a white hat hacker and technology innovator focused on securing and advancing mobile systems that support communication for 7.1 billion users worldwide. From the inception of 1G in 1979 to the future of 6G, each new generation of mobile networks brings not only exciting innovations but also hidden security challenges.

I uncover vulnerabilities in mobile devices, wireless access networks, and core networks, while applying cutting-edge machine learning, deep learning, and statistical techniques to explore the intricate relationship between science, technology, and mobile networks.

Please reach out to collaborate πŸ˜ƒ

Featured Publications
Recent Publications
(2024). IMS is Not That Secure on Your 5G/4G Phones. In Mobicom ‘24.
(2024). Taming the Insecurity of Cellular Emergency Services (9–1-1): From Vulnerabilities to Secure Designs. TNET.
(2023). When Good Turns Evil: Encrypted 5G/4G Voice Calls Can Leak Your Identities. CNS ‘23.
(2023). Handling Data Heterogeneity in Federated Learning via Knowledge Fusion. JIOT.
(2022). Uncovering insecure designs of cellular emergency services (911). In MobiCom ‘22.
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