Kyle Lo (@kylelostat) is a research scientist at the Allen Institute for AI on the Semantic Scholar team, where he works on NLP for scientific text with emphasis on literature discovery, knowledge extraction, and document understanding. He is the co-creator of open datasets for scientific text mining, like CORD-19 and S2ORC, and large modeling resources, like SciBERT. He is also an organizer of the SciNLP and Scholarly Document Processing workshops and the TREC-COVID, EPIC-QA, and SCIVER shared tasks. His work on domain adaptation of language models for science was runner-up for best paper at ACL 2020, and his works on paper summarization, scientific fact checking, and sex bias identification have been featured in MIT Tech Review, Nature, Quartz, VentureBeat, and others. Kyle has an MS in Statistics from the University of Washington.
