Peer-Reviewed Publications


  1. Divide and Conquer: Neuroevolution for Multiclass Classification
    T. McDonnell, S. Andoni, E. Bonab, S. Chang, J. Choi, J. Goode, K. Moore, G. Sellers, J. Schrum
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2018. 
    [ pdf ]
     
  2. What Can Rationales Behind Relevance Judgments Tell Us About Assessor Disagreement?
    M. Kutlu, T. McDonnell, Y. Barkallah, T. Elsayed, M. Lease
    In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 2018. To appear.
    [ pdf ]
     
  3. Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collections Accurately and Affordably. T
    M. Kutlu, T. McDonnell, A. Sheshadri, T. Elsayed, M. Lease
    In Proceedings of the 1st Biannual Conference on the Design of Experimental Search & Information Retrieval Systems (DESIRES), 2018. To appear.
    [ pdf ]
     
  4. Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to Ensure Quality Relevance Annotations.
    T. Goyal, T. McDonnell, M. Kutlu, T. Elsayed, M. Lease
    In Proceedings of the Sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2018. To appear.
    [ pdf ]
     
  5. Synonymy and Antonymy Detection in Distributional Models.
    S. Chaurasia, T. McDonnell
    Technical Report, University of Texas at Austin, (pre-print), 2017. arXiv.
     
  6. Neural Information Retrieval: At the End of the Early Years.
    K. Onal, Y. Zhang, I. Altingovde, M. Rahman, P. Karagoz, A. Braylan, B. Dang, H. Chang, H. Kim, Q. McNamara, A. Angert, E. Banner, V. Khetan, T. McDonnell, A. Nguyen, D. Xu, B. Wallace, M. Rijke, M. Lease
    In Information Retrieval, Springer, 2018.
    [ pdf ]
     
  7. The Many Benefits of Annotator Rationales for Relevance Judgments.
    T. McDonnell, M. Kutlu, T. Elsayed, M. Lease
    In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCIA): Sister Conference Best Paper Track, 2017. [ pdf ]
     
  8. Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments.
    T. McDonnell, M. Lease, M. Kutlu, T. Elsayed
    In Proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016. Best Paper Award.
    pdf ] [ slides ] [ data ] [ blog ] [ press ]
     
  9. Neural Information Retrieval: A Literature Review.
    Y. Zhang, M. Rahman, A. Braylan, B. Dang, H.-L. Chang, H. Kim, Q. McNamara, A. Angert, E. Banner, V. Khetan, T. McDonnell, A. Nguyen, D. Xu, B. Wallace, M. Lease
    Technical Report, University of Texas at Austin, (pre-print), 2016. ArXiv 1611.06792.
    pdf ]
     
  10. An Empirical Study of API Stability and Adoption in the Android Ecosystem.
    T. McDonnell, B. Ray, M. Kim
    In Proceedings of the Twenty-Ninth IEEE International Conference on Software Maintenance (ICSM), 2013.
    pdf ] [ Slides ]

 

Media Publications


 

Projects


  • Multi-Task Deep Representation Learning, 2016
    [ Paper ] [ Code ]
     
  • Crowdsourcing Relevance Judgments Using Reinforcement Learning, 2016
    [ Paper ]
     
  • Synonymy and Antonymy Detection in Distributional Models, 2016
     [ Paper ]
     
  • GASP: The Graduate Admission Support Program, 2013
    Poster ] [ Related ]
     
  • Automatic Segmentation of Neurons in Electron Micrograph Images, 2013
    Slides ]
     
  • Crystal Growth and Analysis of Select Oligothiophenes for Integration in Photovoltaics, 2010
    Poster ]