I am a broadly interdisciplinary artificial intelligence researcher specializing in natural language processing and methods inspired by cognition and the brain. I apply these to application areas in science and health care.
A central focus of my science research is on how we can teach computers question answering in the form of passing standardized science exams, as written. In particular, I focus on methods of automated inference that generate explanations for why the answer is correct, largely using graph-based methods.
In terms of health care, I study how we can use natural language processing and inference to improve electronic health records and improve nurse communication, as well as detect potentially dangerous clinical events before they happen.
I uniquely have two distinct educational backgrounds, one in natural language processing, cognition, and computer science, the other in physics, electrical engineering, and sensing. I maintain active outreach in grounding science education through sensing, largely in the form of open source hardware like the tricorder project, and projects like the open source computed tomography scanner. This work has been widely featured in over 50 international news media articles, including Reuters, Forbes, WIRED, MSNBC, and the Washington Post, as well as an invited talk at TEDxBrussels 2012. In 2015, my open source science tricorder was honoured by being placed on permanent exhibit at the German Museum of Technology in Berlin.
Assistant Professor, School of Information, University of Arizona
office: harvill 437C