We work on problems in natural language processing spanning multiple research themes:

  • Explainable Inference: Design explanation-centered methods for decision making.
  • Common-sense Knowledge: Developing methods for extracting and organizing common-sense knowledge about events.
  • Question Answering: Developing methods for extracting and reasoning with information expressed in natural language.
  • People-centered language processing: Understanding language by modeling the people behind the language.
  • Systems --> NLP: Privacy focused NLP solutions that can run on commodity personal user devices. 
  • NLP --> Systems: Using NLP solutions for assisting translation of natural language specifications to formal verifiable models.



  • [Nov 2017] Paper on event representations accepted at AAAI 2018.

  • [June 2017] Paper on personalizing NLP accepted at EMNLP 2017.

  • [Jun 2017 ] Paper on RNNs for mobile devices at EMDL workshop in MobiSys 2017.

  • [Jan 2017] Paper on privacy-focused AI accepted at HotMobile 2017.

  • [Sep 2016] Paper on explanation-centered analysis of QA accepted at COLING 2016.

  • [July 2016] Teaching grad Artificial Intelligence in Fall 2016.

  • [July 2016] Paper on extracting process knowledge accepted at EMNLP 2016.

  • [Dec 2015] Teaching undergrad NLP [CSE 390] in Spring 2016.

  • [Aug 2015] Paper on process representations accepted at K-CAP Workshop.

  • [Aug 2015] Teaching Advanced Topics in Computational Linguistics in Fall 2015.