Research

Our lab is broadly interested in advancing machine learning and artificial intelligence, and using these to transform health care. We currently have three main focus areas:


Theoretical Machine Learning

We research unsupervised learning, causal inference from observational data, deep learning, time-series modeling, approximate probabilistic inference, structured prediction, and semi-supervised learning algorithms for natural language processing.


Precision Medicine

These are exciting times for the practice of medicine. The rapid adoption of electronic health records and 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 better understand disease progression and to facilitate new, precise treatment strategies for a wide range of diseases and conditions such as Type 2 diabetes, which affects tens of millions of people worldwide every year, and multiple myeloma, a rare blood cancer. In pursuit of these aims, a major methodological focus has been on developing novel approaches to modeling high-dimensional time-series data, particularly approaches that bring together probabilistic modeling and deep learning, and causal inference from observational data.


Intelligent Electronic Health Records

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.