Bio

My recent research explores weakly supervised machine learning, where indirect and often noisy sources of domain knowledge are combined to train models. Obtaining large-scale, expert-labeled training data is a significant challenge in medicine, making it difficult to take advantage of state-of-the-art machine learning tools. Weakly supervised methods enable new mechanisms of sharing medical expertise and generating training sets from large-scale collections of unlabeled text, medical imaging, and sensor data.