Sham Kakade
Harvard SEAS CS · Rampell Family Professor of Computer Science and Professor of Statistics; Co-director, Kempner Institute
Research focus: machine learning theory · foundation models · optimization · reinforcement learning
手动审核申请准备：作为数学严谨的ML申请人：optimization、RL、缩放定律、基础模型训练、智能体或学习理论。
Who this advisor fits / 什么情况下适合你
- You want theory depth and proof/analysis-oriented problem framing.
- Applicants should show either deep applied foundation-model systems experience or strong optimization/math/theory preparation; the page explicitly welcomes both backgrounds.
- 你要的是理论深度、证明/分析导向的问题设定。
- 申请人应展示深厚的应用基础模型系统经验，或具备强 optimization/数学/理论准备；该页面明确欢迎两种背景。
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.
- You need strong placement evidence, but public signals for this PI are still thin.
- 你只想做快速 benchmark / API 拼装，不想读领域论文或做长期实验。
- 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.