Fengjun Li
University of Kansas — Electrical Engineering & Computer Science (EECS) · Professor
Research focus: trustworthy & privacy-preserving federated learning · adversarial machine learning · AI for cyber-threat analysis · IoT / network security
针对申请文档中涉及的人工审核（Manual application-prep audit）：构建基于可信与隐私保护的联邦学习、对抗性ML、网络威胁分析、物联网与网络安全、应用加密、健康医疗安全以及隐私意识AI系统的适配方案。请引用一个可见的项目、实验室主题、资助/招聘信号、课程、研究领域或就业模式，并提出一个具体的第一学期扩展方案。
Who this advisor fits / 什么情况下适合你
- You care about faculty-track outcomes; public alumni rows include verifiable faculty placements.
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- Use the placement pattern to calibrate ambition, but still anchor outreach in the current project fit, advisor capacity, and program route.
- 你在意学术教职/教职轨去向，公开校友里有可核对的教职案例。
- 工业研究或创业/转化路径对你有吸引力（公开去向里占比不低）。
- 请利用位置模式来塑造雄心壮志，但始终将当前项目的契合度、导师能力以及项目路线作为锚点。
What to watch for / 什么情况下要慎重
- Faculty-only is your sole goal and you want zero industry/translation exposure.
- 你把「教职为唯一目标」且完全不想碰工业/转化网络。
Public evidence as of 2026-06-10
These are decision-support signals compiled from public evidence (faculty pages, publications, lab sites) to help you ask better questions — not a ranking, rating, or allegation about this advisor. / 以上为基于公开信息整理的择校参考，帮助你提出更好的问题，并非排名、评分或对该导师的指控。
Ask a verified 学长学姐 / 同校 .edu 认证点评
The thing applicants say only 师兄师姐 can tell you — current & former students of this lab, verified by their school .edu. Open the full dossier to read or add a verified note.