Data to Actionable Knowledge Lab

We are Harvard’s Data to Actionable Knowledge (DtAK) lab, led by Finale Doshi-Velez. We use probabilistic machine learning methods to address many decision-making scenarios, with a focus on healthcare applications. Our work falls into three major areas:

  1. Probabilistic modeling and inference:
    We focus especially on 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:
    We focus especially on 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?