Yan Leng
UT Austin CS · Assistant Professor
Research focus: interpretable machine learning on networks · deep learning on networks · network spillovers and dynamics · human-centric interpretability in generative AI
保护项目 2 的预审审核：在可解释的 ML 上构建与网络拟合、网络溢出、生成性 AI 可解释性相关的拟合图谱，并围绕 AI for mental health、business AI、社交网络等主题进行审查。请引用一个可见的实验室主题、校友/分配模式、基准信号、当前学生名单、招募/状态警告或项目领域，并提出一项具体的第一学期研究扩展。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: interpretable machine learning on netwo… · deep learning on networks).
- Applicants should use publications, grant scope and current course/research themes for fit, and ask directly about doctoral advising and recent student placements.
- 你想把 ML 接到真实数据与落地问题（当前信号：interpretable machine learning on netwo… · deep learning on networks）。
- 申请人应通过出版物、资助范围及当前课程或研究主题来评估匹配度，并直接询问博士指导及近期学生实习安排。
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.
- You need strong placement evidence, but public signals for this PI are still thin.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
- 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.