Finale Doshi-Velez

Kempner Faculty Steering Committee
Herchel Smith Professor in Computer Science

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Contact Information

Subjects I Teach:

  • CS181: Undergraduate Machine Learning
  • CS282: Topics in Machine Learning

Areas I Research:


Finale Doshi-Velez is a Professor in Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.

Research Focus

Doshi-Velez’s current research focuses on probabilistic methods, reinforcement learning, and interpretability. She heads the Data to Actionable Knowledge (DtAK) group at Harvard Computer Science, where the lab uses probabilistic methods to address many decision-making scenarios involving humans and AI. The lab’s work spans specific application domains (health and wellness) as well as broader socio-technical questions around human-AI interaction, AI accountability, and responsible and effective AI regulation and falls into three major areas:

(1) Probabilistic modeling and inference (especially Bayesian models): How can we characterize the uncertainty in large, heterogeneous data?  How can we fit models that will be useful for downstream decision-making?  How can we build models and inference techniques that will behave in expected and desired ways?

(2) Decision-making under uncertainty (especially sequential decision-making): How can we optimize policies given batches of heterogeneous data?  How can we provide useful information, even if we can’t solve for a policy?  How can we characterize the limits of our ability to provide decision support?

(3) Interpretability and statistical methods for validation: How can we estimate the quality of a policy from batch data?  How can we expose key elements of a model or policy for expert inspection?