Kexin Pei
University of Chicago CS · Neubauer Family Assistant Professor of Computer Science
Research focus: machine learning for code · program analysis · software security · AI software reliability
对于 Kexin Pei 的预审审核：申请人应展示 ML 级别代码或程序分析领域的安全工作成果，包括代码模型、漏洞/缺陷检测、软件可靠性或基于 LLM 的开发工具。
Who this advisor fits / 什么情况下适合你
- Applicants should show ML-for-code or program-analysis security work: code models, bug/vulnerability detection, software reliability, or LLM-based developer tools.
- 申请人应展示 ML 级别或程序分析领域的安全工作成果，包括但不限于代码模型、漏洞与缺陷检测、软件可靠性测试，或基于 LLM 的开发者工具。
What to watch for / 什么情况下要慎重
- You need strong placement evidence, but public signals for this PI are still thin.
- 你需要强证据的毕业去向分布，而这位 PI 目前公开信号仍较薄。
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
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