I am an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science at Stanford University. The core goal of my research is to augment clinical decision making and broaden access to high quality healthcare through the use of machine learning (ML) and natural language processing. I leverage heterogeneous clinical data, such as electronic health records and genomic data, to provide actionable insights to clinicians, researchers, and patients and develop new methods to infuse biomedical knowledge found in knowledge graphs and text into machine learning algorithms. My work is motivated by the question: how can we design trustworthy machine learning methods that excel in settings with limited annotated data and can be deployed safely and effectively into clinical workflows?

Previously, I was a postdoctoral fellow at Brigham and Women’s Hospital and Harvard Medical School (HMS) where I worked to deploy ML models within the Mass General Brigham healthcare system. I received my PhD from the Health Science & Technology (HST) program at MIT & HMS, co-advised by Zak Kohane and Pete Szolovits. During my PhD, I created ClinicalBERT, a language model trained on electronic health records that has millions of downloads on HuggingFace, and developed SHEPHERD, a graph neural network approach for the diagnosis of patients with rare genetic diseases in the Undiagnosed Disease Network.