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

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Dr. Nathan Gaw is an Assistant Professor of Data Science at Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA. He received his B.S.E. (2013) and M.S. (2014) in biomedical engineering and a Ph.D. (2019) in industrial engineering from Arizona State University (ASU), Tempe, AZ, USA. Dr. Gaw was a Postdoctoral Research Fellow at the ASU-Mayo Clinic Center for Innovative Imaging, Tempe, AZ, USA (2019-2020), and a Postdoctoral Research Fellow in the School of Industrial and Systems Engineering (ISyE) at Georgia Institute of Technology, Atlanta, GA, USA (2020-2021). He has also served as chair of the INFORMS Data Mining Society, a Council Member of IISE Data Analytics and Information Systems Division, and is a member of IEEE.

His research interests focus on developing new statistical machine learning algorithms to optimally fuse high-dimensional, heterogeneous, and multi-modal data sources to support decision making in the military, remote sensing, and healthcare settings (e.g., telemonitoring, diagnostics, combat recovery, anomaly detection, etc.).
Specific methodological focuses include feature extraction/selection (e.g., tensor decomposition, functional data analysis, etc.), spatio-temporal neural networks (e.g., attention-based ConvLSTMs (convolutional long short-term memory), calibration quantification, etc.), and image registration/fusion (e.g., convolutional neural networks, etc.).
Specific application areas in problems related to defense include aviation human factors (e.g., quantification of pilot cognition), remote sensing of weather events (lightning, tornado, etc.), and cybersecurity (e.g., detecting malicious cyber attacks).

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 Engineering, 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 Engineering, Arizona State University

Barrett Honors College; selected student speaker for the Engineering Convocation

Awards

Distinguished Teaching Professor, Department of Operational Sciences, 2023, AFIT
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

*denotes student author

Peer-Reviewed Journal Articles

I have 16 articles, of which 12 are Q1 and 4 are Q2 according to Scimago Journal Rankings (link).

Crino N*, Cox BA, Gaw N (2024). Garbage In ≠ Garbage Out: Exploring GAN resilience to image training set degradations. Expert Systems with Applications, 123902. Scimago Journal Rank: Q1. https://doi.org/10.1016/j.eswa.2024.123902
Wasilefsky D*, Caballero WN, Johnstone C, Gaw N, Jenkins PR (2024) Responsible Machine Learning of United States Air Force Pilot Candidate Selection. Decision Support Systems. Scimago Journal Rank: Q1. https://doi.org/10.1016/j.dss.2024.114198
Choo N*, Lunday B, Ahner D, Gaw N, Little B, McBee B (In Press) Investigating Regional Communication Network Robustness of an Asymmetric String-of-Pearls Satellite Constellation Design Framework. Journal of Spacecraft and Rockets. Scimago Journal Rank: Q2. https://doi.org/10.2514/1.A35802
Gaw N, Yoon H, Li J (2024) A novel semi-supervised learning model for smartphone-based health telemonitoring. IEEE Transactions on Automation Science and Engineering. 21(1), 428-441. Scimago Journal Rank: Q1. 10.1109/TASE.2022.3218132
- Short-paper version received the INFORMS Data Mining and Decision Analytics (DMDA)Workshop Best Paper (Applied Track)
Caballero WN, Gaw N, Jenkins PR, Johnstone C (2023) Toward Automated Instructor Pilots in Legacy Air Force Systems: Physiology-based Flight Difficulty Classification via Machine Learning, Expert Systems with Applications 231, 120711. Scimago Journal Rank: Q1. https://doi.org/10.1016/j.eswa.2023.120711
Zhao M*, Reisi Gahrooei M, Gaw N (2023) Coupled Tensor Decomposition for Robust Feature Extraction. IISE Transactions on Healthcare Systems Engineering, 13(2), 117-131. Scimago Journal Rank: Q2. https://doi.org/10.1080/24725579.2022.2141929
- Short-paper version was a finalist for the IISE QCRE Best Student Paper Competition
- Article featured in Industrial and Systems Engineering (ISE) Magazine
Gaw N, Yousefi S, Reisi Gahrooei M (2022) Multimodal Data Fusion for Systems Improvement: A Review. IISE Transactions 54(11), 1098-1116. Scimago Journal Rank: Q1. https://doi.org/10.1080/24725854.2021.1987593
Arun NT*, 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 3(6), e200267. Scimago Journal Rank: Q1. https://doi.org/10.1101/2020.07.28.20163899
- Most cited paper in Radiology: Artificial Intelligence in 2022
Yoon H, Gaw N (2021) A novel multi-task linear mixed model for smartphone-based telemonitoring. Expert Systems with Applications, 164, 113809. Scimago Journal Rank: Q1. https://doi.org/10.1016/j.eswa.2020.113809
Chang K*, Beers AL, Brink L, Patel JB*, Singh P, Arun NT*, Hoebel KV*, 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. Scimago Journal Rank: Q1. https://doi.org/10.1016/j.jacr.2020.05.015
Gaw N, Hawkins-Daarud A, Hu LS, et al. (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. Scimago Journal Rank: Q1. https://doi.org/10.1038/s41598-019-46296-4
Gaw N, Schwedt TJ, Chong CD, 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): pp. 36-46. Scimago Journal Rank: Q2. https://doi.org/10.1080/24725579.2017.1403520
- Article featured in Industrial and Systems Engineering (ISE) Magazine
Chong CD, Gaw N, Fu Y, Li J, Wu T, Schwedt TJ (2017) Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia 37(9): pp. 828-844. Scimago Journal Rank: Q1. https://doi.org/10.1177/0333102416652091
- Received the Harold Wolff-John Graham Award from the American Academy of Neurology
Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, et al. (2016) Radiogenomics to characterize regional heterogeneity in glioblastoma. Neuro-Oncology 19(1): pp. 128-137. Scimago Journal Rank: Q1. https://doi.org/10.1093/neuonc/now135
Hu LS, Ning S, Eschbacher JM, Gaw N, et al. (2015) Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PloS One 10(11). Scimago Journal Rank: Q1. https://doi.org/10.1371/journal.pone.0141506
Schwedt TJ, Chong CD, Gaw N, Fu Y, Wu T, Li J (2015) Accurate classification of chronic migraine via brain magnetic resonance imaging. Headache: The Journal of Head and Face Pain 55(6): pp. 762-777. Scimago Journal Rank: Q2. https://doi.org/10.1111/head.12584
- Received the Harold G. Wolff Lecture Award from the American Headache Society

Submitted Journal Articles

Barry G*, Johnstone C, Caballero W, Jenkins P, Chou C, Wang Y, Beauchene C, Rao H, Gaw N (Under Review) Student-Pilot Error Prediction via Multi-modal Physiological Signals and Tree-Based Models. Decision Support Systems. Scimago Journal Rank: Q1.
Choo N, Ahner D, Lunday B, Gaw N (Under Review) Orbital Parameter Determination of Single Satellite Circular Orbits for Regional Coverage Using a Response Surface Methodology. Acta Astronautica. Scimago Journal Rank: Q1.
Choo N, Ahner D, Gaw N, Lunday B (Under Review) Optimal Repeat Parameter Selection for Modifying Orbits from Non-Repeating into Repeating Ground Track Orbits. The Journal of the Astronautical Sciences. Scimago Journal Rank: Q2.

Peer-Reviewed Conference Proceedings

Johnston N*, Gaw N, Wertz J, Cox B, Blasch E, Cherry M, O’Rourke M, Homa L (2024) Novel Deep Learning Image Registration Techniques with Application to Microscopy Images of Metal Alloys. Dynamic Data Driven Application Systems 2024 (DDDAS-2024).
Gu H*, Gaw N, Wang Y, Johnstone C, Beauchene C, Yuditskaya SC, Rao HM, Chou C (2024) H2G2-Net A Hierarchical Heterogeneous Graph Generative Network Framework for Discovery of Multi-Modal Physiological Responses. AAAI 2024 Workshop on Human-Centric Representation Learning.
Reid D*, Champagne L, and Gaw N. (2023) Implementing Efficient Dynamic Threat Avoidance Routing Based on Dijkstra's Shortest Path Algorithm in the Advance Framework for Simulation, Integration, and Modeling (AFSIM). Proceedings of the 2023 Winter Simulation Conference, San Antonio, TX.
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.
Arun NT*, Gaw N (co-first author), Singh P, Chang K, Hoebel KV, Patel J, ... & Kalpathy-Cramer J (2020) Assessing the validity of saliency maps for abnormality localization in medical imaging. arXiv preprint arXiv:2006.00063.

Contributed Books

Gaw N, Pardalos P, Reisi Gahrooei M (2024) Multimodal and Tensor Data Analytics for Industrial Systems Improvement. Springer Optimization and Its Applications (SOIA). Vol. 211.

Patents

Hu LS, Li J, Swanson KR, Wu T, Gaw N, Yoon H, & Hawkins-Daarud A (2024) Methods for using machine learning and mechanistic models for biological feature mapping with multiparametric MRI. U.S. Patent No. 11,861,475. Washington, DC: U.S. Patent and Trademark Office.

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

Education

Awards

Publications

Publication Files