Hima Lakkaraju
Harvard SEAS CS · Assistant Professor, Harvard Business School; Affiliate in Computer Science
Research focus: trustworthy machine learning · interpretable AI · AI safety · fairness and recourse
手动审核申请准备：作为可信且可解释的ML申请人，提供解释、救济、ai的可靠性、稳健的评估以及human-AI collaboration或LLM级别的可靠性。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: trustworthy machine learning · interpretable AI).
- Applicants should show trustworthy ML depth: interpretability, recourse, fairness, privacy, adversarial robustness, LLM evaluation, human-subject or decision-support studies, and evidence of strong pu
- 你想把 ML 接到真实数据与落地问题（当前信号：trustworthy machine learning · interpretable AI）。
- 申请人应展示值得信赖的 ML 深度：可解释性、救济性、ai 性、隐私保护、对抗鲁棒性、LLM 评估、人类 - 主体或决策支持研究，以及强有力的出版物执行证据。
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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