Cho-Jui Hsieh
UCLA CS AI / ML · Associate Professor of Computer Science
Research focus: large-scale machine learning · optimization · adversarial robustness · deep learning
保护级别 2 的自动审核：作为大型 ML / optimization 申请者，采用 adversarial robustness、高效的 ai 推理/训练、模型压缩、neural architecture search 或可扩展的学习算法。
Who this advisor fits / 什么情况下适合你
- You want theory depth and proof/analysis-oriented problem framing.
- You care about faculty-track outcomes; public alumni rows include verifiable faculty placements.
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- 你要的是理论深度、证明/分析导向的问题设定。
- 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。
- 工业研究或创业/转化路径对你有吸引力（公开去向里占比不低）。
What to watch for / 什么情况下要慎重
- You only want fast benchmarks/API hacking, not domain papers or longer experimental loops.
- 你只想做快速 benchmark / API 拼装，不想读领域论文或做长期实验。
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.