Lei Ying
University of Michigan CSE · Professor, Electrical Engineering and Computer Science
Research focus: reinforcement learning · stochastic networks · large-scale graph mining · private data marketplaces
保护编号 Lei Ying 的正式申请预审审计：作为数学成熟度较高的 RL/随机网络申请人，证明、队列/控制直觉、优化、随机图或严谨的 RL 理论比广泛的模型构建更为重要。
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 · stochastic networks).
- Strong fit for mathematically mature applicants in reinforcement learning, stochastic networks, graph mining, and queueing/control-flavored AI systems; admissions signal is unusually explicit.
- 你想把 ML 接到真实数据与落地问题（当前信号：reinforcement learning · stochastic networks）。
- 适合具有成熟数学背景的申请人，特别是针对保护编号 reinforcement learning、stochastic networks 和队列/控制偏好型保护编号 AI 的系统；录取信号尤为明确。
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.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
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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