HCII Seminar Series - Tom Hope
Postdoctoral researcher at The Allen Institute for AI (AI2) and The University of Washington
This event will be available via livestream.
"Harnessing Scientific Literature for Boosting Discovery and Innovation"
In the year 1665, the first academic journal was published. Fast forward to today, there are millions of scientific papers coming out every year. This explosion of knowledge represents an opportunity to accelerate innovation with automated systems that scour the literature for solutions and inspirations. However, it also creates information overload and isolated “research bubbles” that limit discovery and sharing, slowing down scientific progress and cross-fertilization. In this talk, I will present my work toward addressing these large-scale challenges for the future of science. I will overview my approach which consists of identifying key structures in the way scientists think, work and innovate, and using them to build computational representations that power creative innovation systems. These include systems that surface inspirations, recommend novel authors, enable search for challenges, hypotheses and causal relations, and tools for exploration and visualization of collaboration networks.
Tom Hope is a postdoctoral researcher at The Allen Institute for AI (AI2) and The University of Washington, working with Daniel Weld on accelerating scientific discovery and closely collaborating with Eric Horvitz, CSO at Microsoft. Tom completed his PhD with Dafna Shahaf at the Hebrew University of Jerusalem in January 2020. His work has received four best paper awards, appeared in top venues (PNAS, KDD, EMNLP, NAACL, ACL, WSDM, AKBC, IEEE), and received media attention from Nature and Science on his systems for COVID-19 researchers, and from VentureBeat on a novel knowledge discovery system he created as research team lead at Intel. Tom was selected for the 2021 Global Young Scientists Summit and 2019 Heidelberg Laureate Forum and was a member of the KDD 2020 Best Paper Selection Committee.