I am an associate professor in the University of Maryland Computer Science Department (tenure home), Institute of Advanced Computer Studies, iSchool, and Language Science Center. Previously, I was an assistant professor at Colorado's Department of Computer Science (tenure granted in 2017). I was a graduate student at Princeton with David Blei.

My research focuses on making machine learning more useful, more interpretable, and able to learn and interact from humans. This helps users sift through decades of documents; discover when individuals lie, reframe, or change the topic in a conversation; or to compete against humans in games that are based in natural language.

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Recent Publications

  • Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, and Jordan Boyd-Graber. Automatic Song Translation for Tonal Languages. Findings of the Association for Computational Linguistics, 2022. [Translation Examples (with sound)] [Code] [Bibtex]
  • Chenglei Si, Chen Zhao, Sewon Min, and Jordan Boyd-Graber. Re-Examining Calibration: The Case of Question Answering. Findings of Empirical Methods in Natural Language Processing, 2022. [Bibtex]
  • Wanrong He, Andrew Mao, and Jordan Boyd-Graber. Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain QA. Findings of Empirical Methods in Natural Language Processing, 2022. [Bibtex]
  • Shi Feng and Jordan Boyd-Graber. Learning to Explain Selectively: A Case Study on Question Answering. Empirical Methods in Natural Language Processing, 2022. [Bibtex]
  • HyoJung Han, Marine Carpuat, and Jordan Boyd-Graber. Detecting Simultaneous MT Errors through Word-by-Word Question Answering. Empirical Methods in Natural Language Processing, 2022. [Bibtex]
  • Yoshinari Fujinuma, Jordan Boyd-Graber, and Katharina Kann. How Does Multilingual Pretraining Affect Cross-Lingual Transferability?. Association for Computational Linguistics, 2022. [Code] [Bibtex]
  • Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, and Jordan Boyd-Graber. Adapting Coreference Resolution Models through Active Learning. Association for Computational Linguistics, 2022. [Code] [Bibtex]
  • Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. [Preprint] [Bibtex]
  • Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. [Webpage] [Bibtex]
Jordan Boyd-Graber