Matthew Lease
UT Austin CS · Professor, School of Information
Research focus: information retrieval · crowdsourcing · human computation · natural language processing
保护项目 4 的预审审核：构建围绕 information retrieval、crowdsourcing、human computation、fair and explainable AI、NLP、human-centered AI 的拟合图；引用一个可见的实验室主题、校友/安置模式、基准信号、招募/状态警告或当前项目领域，并提出一项具体的第一学期的研究扩展。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: information retrieval · crowdsourcing).
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- Applicants should show human-centered AI instincts: data annotation/crowdsourcing design, IR/NLP evaluation, fairness/explainability and user-impact thinking, not only model-building.
- 你想把 ML 接到真实数据与落地问题（当前信号：information retrieval · crowdsourcing）。
- 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）。
- 申请人应展现出 human-centered AI 般的直觉：数据标注设计、crowdsourcing 级架构设计、NLP 级评估、ai 的敏锐度以及用户影响思维，而不仅仅是模型构建。
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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