Active Research Projects
Recently, I'm motivated by two exciting clinical applications:
- antidepressant recommendations for patients with major depression
- forecasting need for interventions in the Intensive Care Unit (ICU)
These applications have inspired new contributions to machine learning:
Semi-supervised learning: Our new Prediction Constrained training objective fits latent variable models so that they provide accurate predictions (e.g. drug recommendations) and interpretable generative models, even when labeled examples are rare.
Explainable AI: Our new Tree Regularization method lets you optimize deep neural networks so learned class boundaries are similar to decision trees.
My Ph.D. thesis work was motivated by several applied questions:
- can we find clusters of cooccuring words that thematically organize every New York Times article from the last 20 years?
- can we find clusters of cooccuring epigenetic modifiers that amplify or inhibit gene expression?
To answer these questions, we developed new variational inference algorithms for a broad family of Bayesian nonparametric models that include mixtures, topic models, sequential models, and relational models. Our key innovations include scaling to millions of examples and adding data-driven split/merge proposal moves to avoid poor local minima.
Please try out BNPy, our open-source Python package.