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.



  • [Jul 2015] Paper on Markov Logic Networks for QA accepted at EMNLP 2015.

  • [Jul 2015] Paper on Markov Logic Networks for Question Answer accepted at StarAI.

  • [Feb 2015] Paper on the 4th grade Question Answering won a best paper at AKBC in NIPS.