Baharan Mirzasoleiman
UCLA CS AI / ML · Assistant Professor of Computer Science
Research focus: data-efficient machine learning · robust learning · sustainable machine learning · data selection
针对 Baharan Mirzasoleiman 的预审审核：请作为数据高效且稳健的 ML 申请人进行准备：data selection、核心集、数据质量、可维持的 ML、基础模型训练数据、鲁棒性，或理论坚实的高效学习。
Who this advisor fits / 什么情况下适合你
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- Strong fit for applicants with ML theory/systems evidence around data selection, data-efficient learning, robustness, sparsity, or sustainable/foundation-model data quality; explicit recruiting instru
- 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）。
- 对于具备 ML 理论/系统证据、数据高效学习、鲁棒性、稀疏性或可维持性（ai）的申请人，以及具有 foundation-model data quality 的申请人，该申请符合 data selection 理论/系统证据，数据高效学习，鲁棒性，稀疏性，或可维持性（ai）；明确招募指令为 ai 可获取。
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。
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