Danqi Chen

Danqi Chen (simplified Chinese: 陈丹琦; traditional Chinese: 陳丹琦; pinyin: Chén Dānqí, 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 joined the Princeton NLP group, alongside Sanjeev Arora, Christiane Fellbaum, Karthik Narasimhan.[2] She was previously a visiting scientist at Facebook AI Research (FAIR). She earned her Ph.D at Stanford University and her BS from Tsinghua University.[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]

Google's SyntaxNet is based on algorithms developed by Danqi Chen and Christopher Manning at Stanford.[5]

She won a gold medal at the International Informatics Olympiad. She is known among friends as CDQ.[1] A well known algorithm in competitive programming, CDQ Divide and Conquer, is named after this acronym.[6] Her primary research interests are in text understanding and knowledge representation and reasoning.[7]

References

  1. "Danqi Chen's Homepage". cs.stanford.edu. Retrieved 2019-05-06.
  2. "Princeton NLP". nlp.cs.princeton.edu. Retrieved 2019-05-06.
  3. Danqi Chen (2018). Neural Reading Comprehension and Beyond. Stanford University Press. 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. arXiv:1704.00051. Bibcode:2017arXiv170400051C. doi:10.18653/v1/P17-1171.
  5. Ray, Tiernan. "The question of AI for ServiceNow is a question of what works".
  6. "CDQ Divide and Conquer (Learning Notes)".
  7. "Danqi Chen's Homepage". www.cs.princeton.edu. Retrieved 2020-05-24.




This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.