Jonathan Bragg
Ph.D. Student
Computer Science & Engineering
University of Washington

jbragg [at]

I am a fifth-year Ph.D. student in Computer Science & Engineering at the University of Washington, advised by Dan Weld and Mausam. My primary research interests are in artificial intelligence, machine learning, and human-computer interaction. In particular, I am interested in making crowdsourcing more broadly useful by reducing the effort required to design and deploy crowdsourcing systems.

Before coming to UW, I completed my undergraduate degree at Harvard College in 2010, and also received a Master's in oboe performance from New England Conservatory through their dual degree program with Harvard.


Conference Papers
  • Meredith Ringel Morris, Jeffrey P. Bigham, Robin Brewer, Jonathan Bragg, Anand Kulkarni, Jessie Li, Saiph Savage. 2017. Subcontracting Microwork. In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI '17). Denver, CO, USA.
  • Ryan Drapeau, Lydia B. Chilton, Jonathan Bragg, Daniel S. Weld. 2016. MicroTalk: Using Argumentation to Improve Crowdsourcing Accuracy. In Proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP '16). Austin, TX, USA.
  • Angli Liu, Stephen Soderland, Jonathan Bragg, Christopher H. Lin, Xiao Ling, Daniel S. Weld. 2016. Effective Crowd Annotation for Relation Extraction. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL '16). San Diego, CA, USA.
  • Jonathan Bragg, Mausam, and Daniel S. Weld. 2016. Optimal Testing for Crowd Workers. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS '16). Singapore.
    [pub | code | talk]
  • Shih-Wen Huang*, Jonathan Bragg*, Isaac Cowhey, Oren Etzioni, Daniel S. Weld. 2016. Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW '16). San Francisco, CA, USA.
    [pub | talk]
  • Jonathan Bragg, Andrey Kolobov, Mausam, and Daniel S. Weld. 2014. Parallel task routing for crowdsourcing. In Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP '14). Pittsburgh, PA, USA.
    [pub | talk]
  • Jonathan Bragg, Mausam, and Daniel S. Weld. 2013. Crowdsourcing multi-label classification for taxonomy creation. In Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing (HCOMP '13). Palm Springs, CA, USA.
    Best Paper Award.
  • Jonathan Bragg, Elaine Chew, and Stuart Shieber. 2011. Neo-Riemannian cycle detection with weighted finite-state transducers. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR '11). Miami, FL, USA.
    [pub | poster]
Book Chapters
  • Daniel S. Weld, Mausam, Christopher H. Lin, Jonathan Bragg. 2015. Artificial Intelligence and Collective Intelligence. In Thomas Malone & Michael Bernstein (Eds.), The Collective Intelligence Handbook. MIT Press.
Workshop Papers
  • Jonathan Bragg, Mausam, and Daniel S. Weld. 2015. Learning on the Job: Optimal Instruction for Crowdsourcing. In ICML '15 Workshop on Crowdsourcing and Machine Learning. Lille, France.
Posters and Presentations
  • Anand Sriraman, Jonathan Bragg, and Anand Kulkarni. 2017. Worker-Owned Cooperative Models for Training Artificial Intelligence. In CSCW '17 Poster Track. To appear.
  • Jonathan Bragg, Mausam, and Daniel S. Weld. 2013. Crowdsourcing multi-label classification. Poster and oral presentation at CrowdConf Research Track. San Francisco, CA, USA.
    [abstract | poster | talk]
Theses and Misc
  • Jonathan Bragg and Cheng-Zhi Anna Huang. 2010. Mathematics and Computation in Music 2009: John Clough Memorial Conference. Computer Music Journal, 34(1):100-102.
    [pub | html]
  • Jonathan Bragg. 2010. Detection of Neo-Riemannian Cycles: A Finite State Approach. Honors bachelor's thesis, Departments of Computer Science and Music, Harvard University.