Daniel Diaz
UT Austin CS · Postdoctoral Research Fellow, Leads Deep Proteins Group
Research focus: AI for protein engineering · bioinformatics · computational biology · machine learning
手动审核申请准备：构建与AI for protein engineering、bioinformatics、computational biology、machine learning、protein design、biomolecular engineering 相匹配的拟合路径；引用一个可见的项目、实验室主题、招募信号、校友/就业模式、资金/课程信号或发表领域，并提出一个具体的第一学期扩展方案。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: AI for protein engineering · bioinformatics).
- Use the admissions-prep section for fit signals and ask directly about formal advising/co-advising status.
- 你想把 ML 接到真实数据与落地问题（当前信号：AI for protein engineering · bioinformatics）。
- 请查阅申请指南中的适应与准备部分，以评估您的契合度，并直接询问关于正式咨询或联合咨询的当前状态。
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.