I am a Postdoctoral Investigator at the Allen Institute for AI (AI2) in the Semantic Scholar research group.

I work on KR and biomedical ontologies, text mining and NLP for biomedical and scientific text, open access, and meta-science. I completed my PhD in the Department of Biomedical Informatics and Medical Education (BIME) at the University of Washington in Seattle, WA. I also hold degrees in Biomedical Engineering and Physics from Johns Hopkins and MIT respectively.

I live on (in?) Capitol Hill in Seattle, WA. In my spare time, I enjoy cycling and hiking, foraging for mushrooms, playing go (weiqi), cooking, and working on miscellaneous projects. See photos from my latest exploits.


Sep 14, 2021: We launched a public beta of Paper to HTML, which rerenders scientific papers as HTML on-demand.

Sep 7, 2021: I gave a talk at the Conference on AI and Theorem Proving (AITP) on "Mathematics in the Scholarly Literature."

Aug 26, 2021: Our paper on medical multi-document summarization and literature review automation has been accepted to EMNLP 2021! Led by intern Jay DeYoung.

Aug 13, 2021: My team won the AI2 Hackathon Common Good award with our project "A11y2: making research accessible, AI2 and beyond"! We developed a way to create accessible HTML renders of paper PDFs on request, which we're hoping to launch publicly soon. Awesome collab with teammates Alex Buraczynski, Daniel King, Matt Latzke, and Sam Skjonsberg.

Jul 29, 2021: I gave a talk at the Science of Science Summer School (S4) on NLP and scientific text mining.

Jul 16, 2021: Our demo paper "SciA11y: Converting Scientific Papers to Accessible HTML" has been accepted to ASSETS 2021!

Jun 22, 2021: I participated in a panel on "Biomedical Informatics Career Development" at the annual NLM Informatics Training Conference, which I last attended as an NLM informatics trainee in 2017!

Jun 22, 2021: New preprint out on arXiv: "Incorporating Visual Layout Structures for Scientific Text Classification, which investigates ways of injecting visual layout structure into language models to improve document understanding! Led by Zejiang (Shannon) Shen

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