Soheil Feizi
University of Maryland CS · Associate Professor
Research focus: trustworthy AI · machine learning · AI safety · computer vision
保护项目 2 的预审审核：构建基于 trustworthy AI 的拟合图谱，确保 machine learning、AI safety 及基础模型可靠性、可追溯性、去学习能力等关键要素；引用一个可见的实验室主题、校友/就业模式、基准信号、当前学生名单、招募/状态警示或项目领域，并提出一个具体的第一学期研究扩展。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: trustworthy AI · machine learning).
- Use the current roster and recent graduated names to assess lab size and topic fit, then second-check individual alumni pages before making placement inferences.
- 你想把 ML 接到真实数据与落地问题（当前信号：trustworthy AI · machine learning）。
- 请依据当前名单及近期毕业人员信息，评估实验室规模与主题契合度，随后在做出初步推断前，再次核对每位校友的个人主页。
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.
- You need strong placement evidence, but public signals for this PI are still thin.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
- 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
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.