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Publications

Below are a list of works from or related to the CrowdEEG project: 

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.