David Jacobs
University of Maryland CS · Professor
Research focus: computer vision · machine learning · object recognition · visual reconstruction
手动审核申请准备：以计算机视觉申请人标准进行准备，具备经典鲁棒性偏好：object recognition、重建、光照变化、结构从动、图像聚类或面向用户/现场部署的CV系统。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: computer vision · machine learning).
- Use this as a CV/ML fit card with a caveat: current advising is source-visible, but alumni placement comparisons need separate second-checking beyond this pass.
- 你想把 ML 接到真实数据与落地问题（当前信号：computer vision · machine learning）。
- 请将此作为 CV/ML 的适配卡，并附注：当前指导处于可见状态，但校友安置的比较需进行二次核对，超出本次通过。
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.