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Heng Ji
Heng Ji

Heng Ji

(217) 244-0862
3318 Siebel Center for Comp Sci

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Heng Ji is a professor at Computer Science Department of University of Illinois at Urbana-Champaign. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Information Extraction and Knowledge Base Population. She is selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, PACLIC2012 Best paper runner-up, "Best of ICDM2013" paper award, "Best of SDM2013" paper award, ACL2018 Best Demo paper nomination, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She is the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing. She has served as the Program Committee Co-Chair of NAACL-HLT2018, NLP-NABD2018, NLPCC2015, CSCKG2016 and CCL2019, and senior area chair for many conferences. She has led several multi-institute research efforts including DARPA DEFT Tinker Bell team of seven universities and DARPA KAIROS RESIN team of six universities. She is the task leader of the U.S. ARL projects on information fusion and knowledge networks construction between 2009-2019. She is invited by the Secretary of the Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030.

Research Interests

  • Natural Language Processing and its connections with Data Mining, Social Science and Vision.

Articles in Conference Proceedings


  • AI's 10 to Watch, IEEE Intelligent Systems (2013)
  • NSF CAREER Award (2009)

Courses Taught

  • CS 598 - Info Extr and Knowledge Acq
  • CS 598 - Knowledge-driven Nat Lang Gen