Invited Talk 1

I'm currently a research scientist in the Shah Lab at Stanford University. Previously I was a CS postdoc in Stanford's Mobilize Center, mentored by Chris Ré and Scott Delp. 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.