Research

Our lab is broadly interested in advancing machine learning and artificial intelligence, and using these to transform health care. Here we explain our three broad focus areas as well as representative papers. For more papers, see our publications section.

  • Clinical Prediction
  • Probabilistic and Causal Inference
  • Medical Knowledge Extraction


    • Clinical Prediction

    These are exciting times for the practice of medicine. The rapid adoption of electronic health records has created a wealth of new data about patients, which is a goldmine for improving our understanding of human health. Our lab develops algorithms that use this data to make better clinical predictions in areas like antibiotic resistance, multiple myeloma, Parkinson’s disease, and other chronic illnesses. In addition, we are concerned with efforts around fairness and interpretability to ensure accurate, useful, and equitable clinical predictions.



    Probabilistic and Causal Inference

    Probabilistic inference is one of the cornerstones of machine learning. Whether for parameter inference at training time or answering queries at test time, we build new inference algorithms for inference in undirected and directed graphical models along with tools to analyze their efficacy. We work on probabilistic inference in deep generative models by developing new inference networks that learn to amortize approximate variational inference. In many instances, the quantity of interest within a Bayesian network is of a causal nature. To that end, our lab develops novel methods for answering causal queries that work effectively with high-dimensional data.



    Medical Knowledge and Extraction

    Today’s electronic health records are predominately a place for recording a patient’s health data. We aim to develop the foundation for the next-generation of intelligent electronic health records, where machine learning and artificial intelligence is built-in to help with medical diagnosis, automatically trigger clinical decision support, personalize treatment suggestions, autonomously retrieve relevant past medical history, make documentation faster and higher quality, and predict adverse events before they happen. A major challenge is the need for robust machine learning algorithms that are safe, interpretable, can learn from little labeled training data, understand natural language, and generalize well across medical settings and institutions.