Dr. Nathan B. Gaw, Assistant Professor of Data Science

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Published or Accepted Journal Papers

  1. Zhao M, Reisi Gahrooei M, Gaw N (In Press) Coupled Tensor Decomposition for Robust Feature Extraction. IISE Transactions on Healthcare Systems Engineering.
    • Short-paper version was a finalist for the IISE QCRE Best Student Paper Competition
  2. Gaw N, Yoon H, Li J (In Press) A novel semi-supervised learning model for smartphone-based health telemonitoring. IEEE Transactions on Automation Science and Engineering.
    • Short-paper version received the INFORMS Data Mining and Decision Analytics (DMDA)Workshop Best Paper (Applied Track)
  3. Gaw N, Yousefi S, Reisi Gahrooei M (2022) Multimodal Data Fusion for Systems Improvement: A Review. IISE Transactions 54(11), 1098-1116.
  4. 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
  5. Gaw, N., Yousefi, S., & Reisi Gahrooei, M. (In Press). Multimodal Data Fusion for Systems Improvement: A Review. IISE Transactions.
  6. 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
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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. Wertz J, Blasch E, Cherry M, O’Rourke S, Scarnati T, Lorenzo N, Homa L, Gaw N (2022) Methods of Scanning Acoustic Microscopy and Eddy Current Fusion for Materials Analysis. Signal Processing, Sensor/Information Fusion, and Target Recognition (SPIE) XXXI, Vol. 12122.
  2. Caballero WN, Gaw N, Jenkins PR, Johnstone C (In Review) Toward Automated Instructor Pilots in Legacy Air Force Systems: Physiology-based Flight Difficulty Classification via Machine Learning, Expert Systems with Applications.

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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