Atul Prakash
University of Michigan CSE · Professor, Electrical Engineering and Computer Science; Richard H. Orenstein Division Chair of Computer Science and Engineering
Research focus: secure machine learning · adversarial machine learning · security and privacy · efficient machine learning
手动审核申请准备：作为安全与隐私增强型 ML 系统的申请人，需评估对抗性 ML、安全高效的 ML、模型鲁棒性、LLM 安全性或 emerging systems security。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: secure machine learning · adversarial machine learning).
- You care about faculty-track outcomes; public alumni rows include verifiable faculty placements.
- Strong fit for self-directed applicants in adversarial ML, secure/efficient ML systems, privacy/security, and systems security; the mentoring plan is a useful fit screen.
- 你想把 ML 接到真实数据与落地问题（当前信号：secure machine learning · adversarial machine learning）。
- 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。
- 适合自主规划申请者的自定向适配者，具备对抗性ML、安全高效的ML系统、隐私与安全性，以及系统安全领域的专业匹配度；导师计划是合适的适配筛选器。
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.