Dr. Nathan B. Gaw, Assistant Professor of Operations Research

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DSN: 785-3636 x4791
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Publications

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Published or Accepted Journal Papers

  1. Arun, N. T., Gaw, N. (co-first author), Singh, P., Chang, K., Aggarwal, M., ... & Kalpathy-Cramer J. (2021). Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiology: Artificial Intelligence, e200267. https://pubs.rsna.org/doi/abs/10.1148/ryai.2021200267
  2. Gaw, N., Yousefi, S., & Reisi Gahrooei, M. (In Press). Multimodal Data Fusion for Systems Improvement: A Review. IISE Transactions.
  3. Yoon, H., & Gaw, N. (2021). A novel multi-task linear mixed model for smartphone-based telemonitoring. Expert Systems with Applications, 113809. doi.org/10.1016/j.eswa.2020.113809
  4. Chang, K., Beers, A. L., Brink, L., Patel, J. B., Singh, P., Arun, N. T., Hoebel, K.V., Gaw, N., ... & Tilkin, M. (2020). Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology. https://doi.org/10.1016/j.jacr.2020.05.015.
  5. Gaw, N., Hawkins-Daarud, A., Hu, L. S., Yoon, H., Wang, L., Xu, Y., … & Gonzales, A. (2019). Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Nature Scientific Reports, 9(1), 10063. https://doi.org/10.1038/s41598-019-46296-4.
  6. Gaw, N. (2019). Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability In Healthcare Applications (Doctoral dissertation, Arizona State University). https://repository.asu.edu/attachments/221561/content/Gaw_asu_0010E_19135.pdf.
  7. Gaw, N., Schwedt, T. J., Chong, C. D., Wu, T., & Li, J. (2018). A clinical decision support system using multi-modality imaging data for disease diagnosis. IISE Transactions on Healthcare Systems Engineering, 8(1), 36-46. https://doi.org/10.1080/24725579.2017.1403520.
    • This paper was selected as a Feature Article among papers published in this issue.
  8. Chong, C. D., Gaw, N., Fu, Y., Li, J., Wu, T., & Schwedt, T. J. (2017). Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia, 37(9), 828-844. https://doi.org/10.1177/0333102416652091.
    • This paper received the Harold Wolff-John Graham Award from the American Academy of Neurology.
  9. Hu, L. S., Ning, S., Eschbacher, J. M., Baxter, L. C., Gaw, N., Ranjbar, S., … & Nakaji, P. (2016). Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-oncology, 19(1), 128-137. https://doi.org/10.1093/neuonc/now135.
  10. Hu, L. S., Ning, S., Eschbacher, J. M., Gaw, N., Dueck, A. C., Smith, K. A., … & Tran, N. (2015). Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PloS one, 10(11), e0141506. https://doi.org/10.1371/journal.pone.0141506.
  11. Schwedt, T. J., Chong, C. D., Wu, T., Gaw, N., Fu, Y., & Li, J. (2015). Accurate classification of chronic migraine via brain magnetic resonance imaging. Headache: The Journal of Head and Face Pain, 55(6), 762-777. https://doi.org/10.1111/head.12584.
    • This paper received the Harold G. Wolff Lecture Award from the American Headache Society.

Submitted Journal Papers

  1. Gaw, N., Yoon, H., & Li, J. (In Review) A novel semi-supervised learning model for smartphone-based health telemonitoring. IEEE Transactions on Automation Science and Engineering.
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      • Short-paper version received the INFORMS Data Mining and Decision Analysis Workshop Best Paper (Applied Track)

Conference Papers

  1. Arun, N. T., Gaw, N. (co-first author), Singh, P., Chang, K., Hoebel, K. V., Patel, J., ... & Kalpathy-Cramer, J. (2020). Assessing the validity of saliency maps for abnormality localization in medical imaging. arXiv preprint arXiv:2006.00063. https://arxiv.org/abs/2006.00063.
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