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Publications

Below are a list of peer-reviewed research publications related to the CrowdEEG project: 

  1. Schaekermann, M., Beaton, G., Sanoubari, E., Lim, A., Larson, K., & Law, E. (2020). Ambiguity-aware AI Assistants for Medical Data Analysis. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems – CHI ’20. Honolulu, HI, USA: ACM Press.
  2. Schaekermann, M., Beaton, G., Habib, M., Lim, A., Larson, K., & Law, E. (2019). Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication. In Proceedings of the 2019 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2019) (Vol. 3, pp. 1–23). Austin, TX.
  3. Williams, J., Cisse, F.A., Schaekermann, M., Sakadi, F., Tassiou, N.R., Hotan, G., Bah, A.K., Hamani, A.B.D., Lim, A., Leung, E.C.W., Fantaneau, T.A., Milligan, T., Khatri, V., Hoch, D., Vyas, M., Lam, A., Cohen, J., Vogel, A., Law, E., & Mateen, F. (2019). Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications. In Seizure 2019.
  4. Schaekermann, M., Beaton, G., Habib, M., Lim, A, Larson, K. & Law, E. (2019). crowdEEG: A Platform for Structured Consensus Formation in Medical Time Series Analysis. In 8th Workshop on Interactive Systems in Healthcare (WISH), at CHI 2019. Glasgow, UK.
  5. Schaekermann, M., Beaton, G., Habib, M., Lim, A, Larson, K. & Law, E. (2019). Capturing Expert Arguments from Medical Adjudication Discussions in a Machine-readable Format. In 2nd Workshop on Subjectivity, Ambiguity and Disagreement (SAD) on the Web, at WWW 2019. San Francisco, USA.
  6. Williams, J., Cisse, F.A., Schaekermann, M., Sakadi, F., Tassiou, N.R., Bah, A.K., Hamani, A.B.D., Lim, A., Leung, E.C.W., Fantaneau, T.A., Milligan, T., Khatri, V., Hoch, D., Vyas, M., Lam, A., Hotan, G., Cohen, J., Law, E., & Mateen, F. (2019). Utilizing a wearable smartphone-based EEG for pediatric epilepsy patients in the resource poor environment of Guinea: A prospective study. In Annual Meeting of the American Academy of Neurology (AAN) 2019. Philadelphia, USA.
  7. Schaekermann, M., Goh, J., Larson, K. & Law, E. (2018). Resolvable vs. Irresolvable Disagreement: A Study on Worker Deliberation in Crowd Work. In 21st International Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2018). New York, USA.
  8. Goh, J., Mohareb, M., Lim, A., & Law, E. (2018). MechanicalHeart: A Human-Machine Framework for the Classification of Phonocardiograms. In 21st International Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2018). New York, USA.
  9. Schaekermann, M., Law, E., Larson, K., & Lim, A. (2018). Expert Disagreement in Sequential Labeling: A Case Study on Adjudication in Medical Time Series Analysis. In 1st Workshop on Subjectivity, Ambiguity and Disagreement (SAD) in Crowdsourcing, at HCOMP 2018. Zurich, Switzerland.
  10. Jaini, P., Chen, Z., Carbajal, P., Law, E., Middleton, L., Regan, K., Schaekermann, M., Trimponias, G., Tung, J., & Poupart, P. (2017). Online Bayesian Transfer Learning for Sequential Data Modeling. In 5th International Conference on Learning Representations (ICLR 2017). Toulon, France.
  11. Thodoroff, P., Pineau, J., & Lim, A. (2016). Learning Robust Features using Deep Learning for Automatic Seizure Detection. In Machine Learning in Health Care. Los Angeles, CA.
  12. Pan, S., Larson, K., Bradshaw, J., & Law, E. (2016). Dynamic Task Allocation Algorithm for Hiring Workers that Learn. In 25th International Joint Conference on Artificial Intelligence (IJCAI-16). New York City, NY.
  13. Schaekermann, M., Law, E., Williams, A.C., & Callaghan, W. (2016). Resolvable vs. Irresolvable Ambiguity: A New Hybrid Framework for Dealing with Uncertain Ground Truth. In 1st Workshop on Human-Centered Machine Learning (HCML), at CHI 2016. San Jose, USA.