Abstract

Abstract: With over one million papers added every year to the PubMed biomedical index alone — the explosion of scholarly knowledge presents tremendous opportunities for accelerating research across the sciences. However, the complexity of scientific literature presents formidable challenges for existing AI and NLP technologies, limiting our ability to tap into this vast treasure trove of information. In this talk, I will present our recent work toward helping researchers and clinicians make use of knowledge embedded in the literature. I will highlight methods that help discover new directions and solutions to problems, generate hypotheses, make predictions and decisions, and build connections across different ideas and areas. This includes models that predict clinical outcomes of hospital patients and new links in biomedical knowledge graphs, a novel scientific information retrieval method that achieves state-of-the-art results, and challenging new datasets for scientific IE. I will also present recent exploratory search and recommendation engines we have developed, and discuss important challenges and opportunities toward AI-powered augmentation of human scientists.

Bio: Tom Hope is a new assistant professor at The Hebrew University of Jerusalem’s School of Computer Science and Engineering, and a visiting research scientist at The Allen Institute for AI (AI2). Tom was awarded the 2022 Azrieli Early Career Faculty Fellowship which is given to eight scientists across all fields of study. Prior to that he was a postdoctoral researcher at AI2 and the University of Washington (UW), working with Daniel Weld and Eric Horvitz. His work has received four best paper awards, appeared in top AI, NLP and HCI venues, and received coverage from Nature and Science.