Kwang-Sung Jun
University of Arizona — Computer Science (Tucson) · Assistant Professor
Research focus: bandits / reinforcement learning · online learning · interactive ML · learning theory
手动审核申请准备：构建基于交互式机器学习、策略学习、强化学习、贝叶斯优化、主动学习、置信区间以及 LLM 后处理、rag 图表等方法的拟合模型，并围绕 chi 进行迭代；引用一个可见的项目、实验室主题、招募信号、校友/就业模式、资金/项目信号或发表领域，并提出一个具体的第一学期扩展方案。
Who this advisor fits / 什么情况下适合你
- 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).
- Use the placement pattern to calibrate ambition where explicit rows exist, but still anchor outreach in current project fit, advisor capacity, and program route.
- 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）。
- 请依据放置模式校准抱负，在显式行存在时以此校准目标，同时始终锚定当前项目契合度、导师容量及项目路线。
What to watch for / 什么情况下要慎重
- Faculty-only is your sole goal and you want zero industry/translation exposure.
- 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。
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.