Lei Xing
Stanford CS
Research focus: radiomics and machine learning in medical imaging
手动审核申请准备：申请人应作为医学生理cs、放射医学cs、医学影像cs、治疗规划AI或临床翻译ML类型的申请人进行准备。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: radiomics and machine learning in medic… · Medical Imaging Techniques and Applicat…).
- You care about faculty-track outcomes; public alumni rows include verifiable faculty placements.
- Strong for AI medicine and radiation oncology ML. Applicants should compare outcomes against medical-physics norms, not only CS faculty placement.
- 你想把 ML 接到真实数据与落地问题（当前信号：radiomics and machine learning in medic… · Medical Imaging Techniques and Applicat…）。
- 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。
- 对于 AI 领域的医学与放射肿瘤学，申请人应比较其临床结果与医学 - 生理学的标准，而非仅关注 CS 学科 faculty 的安置情况。
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。
Strong public placement signal · 公开去向信号：强
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.