Corina S. Păsăreanu
CMU SCS
Research focus: adversarial robustness in machine learning
手动审核申请准备：构建形式化的eth模或软件安全实体：验证器、符号执行器、 fuzzing/repair 系统、测试合成工具或LLM代码安全评估器。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: adversarial robustness in machine learn… · Software Testing and Debugging Techniqu…).
- Applicants should read this as a fit-and-project signal, not a historical placement signal. The bar is likely strongest for formal methods plus modern AI/software-security research, with evidence thro
- 你想把 ML 接到真实数据与落地问题（当前信号：adversarial robustness in machine learn… · Software Testing and Debugging Techniqu…）。
- 申请人应将其视为适合与项目的信号，而非历史分配信号。正式 meth 类项目与现代 mAI/软件安全研究领域的契合度最强，证据应来源于系统论文、证明或验证工具。
What to watch for / 什么情况下要慎重
- You want pure theory ML with little domain data or cross-disciplinary collaboration.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
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.