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

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Dr. Nathan Gaw is an Assistant Professor of Operations Research in the Department of Operational Sciences at Air Force Institute of Technology (AFIT). His research develops new statistical machine learning algorithms to optimally fuse high-dimensional, heterogeneous, multi-modality data sources generated in the healthcare & military settings, such as imaging, genomics, and clinical records, in order to support medical decision making for telemonitoring, diagnostics, and combat recovery.

Personal Website: https://www.nathanbgaw.com

Education

Ph.D., Industrial Engineering, Arizona State University, 2019

Dissertation: Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications (Chair: Prof. Jing Li)

M.S., Biomedical Engineeirng, Arizona State University

Thesis: The Role of Tactile Information in Transfer of Learned Manipulation Following Changes in Degrees of Freedom (Chair: Marco Santello)

B.S.E., Biomedical Engineeirng, Arizona State University

Barrett Honors College; selected student speaker for the Engineering Convocation

Awards

  • Cogpilot Datathon Challenge (hosted by AFWERX), 2021, nationwide challenge to predict flight difficulty and pilot error using multimodal sensor data (e.g., heart rate and eye-tracking), of the 10 awards my team was eligible for, we won 5:
    • Flight Difficulty Prediction (Best Model)
    • Pilot Error Regression (Runner-Up)
    • Most Innovative Approach
    • Most Interpretable Model
    • Best Pitch
  • Best Paper Award (Applied Track), 2019, INFORMS Data Mining and Decision Analytics Workshop
  • Achievement Awards for College Scientists (ARCS), 2019, awarded to 35 Ph.D. students in the state of Arizona for excellent scientific research and academic achievement
  • Industrial Engineering Outstanding TA Award, 2019, ASU
  • Excellent Reviewer Recognition, 2019, NeurIPS Machine Learning for Health Workshop; awarded to top 5% rated reviewers
  • ASU-Mayo Clinic Center for Innovative Imaging Travel Grant, 2019
  • INFORMS Principal Cup, 2nd place, 2018, an international challenge hosted by INFORMS; using historic data and operations research tools, participants were challenged to develop an objective decision-making process to buy, sell, or hold stocks that experience significant events
  • INFORMS ASU Student Research Presentation Competition, 2nd place, 2018
  • Graduate College Fellowship, 2018, ASU
  • Graduate and Professional Student Association Travel Grant, 2017 & 2018, ASU
  • School of Computing, Informatics, and Decision Systems Engineering (SCIDSE) Doctoral Fellowship, 2017, ASU
  • Headache Trainees Tournament (International Headache Conference), 2017, conference abstract chosen as the top 3 out of 99 submissions of doctoral students to participate in a presentation tournament at the one of the largest conferences in headache medicine
  • Harold Wolff-John Graham Award (Best Paper), 2016, American Academy of Neurology
  • Harold G. Wolff Lecture Award (Best Paper), 2015, American Headache Society
  • Dean's Fellowship, 2014, ASU
  • Tau Beta Pi Fellowship, 2013-2014, ASU

Publications

Google Scholar          ResearchGate

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.
    •  
      • 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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