Peter Stone
UT Austin CS · Department Chair, Professor, Truchard Foundation Chair in Computer Science and University Distinguished Teaching Professor
Research focus: reinforcement learning · multiagent systems · robotics · multiagent learning
手动审核申请准备：构建旗舰级 RL、robotics 或多智能体项目，并包含基线、消融实验、统计不确定性 ai 等指标，提供开源代码，以及机器人或模拟器证据。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: reinforcement learning · multiagent systems).
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- Applicants should show unusually concrete RL/robotics/multiagent work: first-author-style projects, reproducible systems, robot or simulator evidence, and a clear fit with LARG/Austin Villa style long
- 你想把 ML 接到真实数据与落地问题（当前信号：reinforcement learning · multiagent systems）。
- 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）。
- 申请人应展示异常具体的多智能体工作：首先作者风格的项目、可重复的系统、机器人或仿真证据，以及清晰契合 LARG/Austin Villa 风格长时域自主性。
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.
- Faculty-only is your sole goal and you want zero industry/translation exposure.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
- 你把「教职为唯一目标」且完全不想碰工业/转化网络。
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。
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