Danqi Chen

From WikiAlpha
Revision as of 15:01, 19 July 2019 by Geo Swan (Talk | contribs) (trim metadata incompatible with this site)

Jump to: navigation, search
The below content is licensed according to Creative Commons Attribution-ShareAlike License contrary to the public domain logo at the foot of the page. It originally appeared on http://en.wikipedia.org. The original article might still be accessible here. You may be able to find a list of the article's previous contributors on the talk page.

Danqi Chen (陈丹琦, IPA: [ʈ͡ʂʰə̌n tan t͡ɕʰi]; born in Changsha, China) is a Chinese-American computer scientist and assistant professor at Princeton University specializing in the AI field of NLP.[1] In 2019, she recently joined the Princeton NLP group, alongside Sanjeev Arora, Christiane Fellbaum, Karthik Narasimhan.[2] She is currently visiting Facebook AI Research. She earned her Ph.D at Stanford University and her BS from Qinghua University, taking classes with Turing award-winner Andrew Yao (also former professor at Princeton).[1]

Chen is the author of Neural Reading Comprehension and Beyond, a book on using artificial intelligence to access knowledge in ordinary and structured documents.[3] She is the author or co-author of a number of journal articles, including Reading Wikipedia to Answer Open-Domain Questions.[4]

Early life

She won a gold medal at the International Informatics Olympiad. She is known among friends as CDQ.[1]

References

  1. 1.0 1.1 1.2 "Danqi Chen's Homepage". https://cs.stanford.edu/~danqi/. 
  2. "Princeton NLP". http://nlp.cs.princeton.edu/. 
  3. Danqi Chen (2018). Neural Reading Comprehension and Beyond. Stanford University Press. https://books.google.ca/books?id=V7s_wAEACAAJ&dq=Danqi+Chen&hl=en&sa=X&ved=0ahUKEwjb0-Tsxr_jAhXMB80KHVEvBycQ6AEIMTAB. Retrieved 2019-07-18. 
  4. Danqi Chen; Adam Fisch; Jason Weston; Antoine Bordes (2017-04-28). "Reading Wikipedia to Answer Open-Domain Questions". Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: 1870–1879.. doi:https://doi.org/10.18653/v1/P17-1171.. https://arxiv.org/pdf/1704.00051.pdf. Retrieved 2019-07-18. "Using Wikipedia articles as the knowledge source causes the task of question answering (QA) to combine the challenges of both large-scale open-domain QA and of machine comprehension of text.". 
[create] Documentation
Template:Compu-scientist-stub