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

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• Publications
• Work History
  Professional Memberships Board Memberships
• Teaching Interests
  PhD Dissertations Advised MS Thesis Advised Undergrad Research Students Courses
• Presentations
  Conferences (invited) Conferences (organized)

MS Thesis Advised

Committee Chair for 14 MS Students (5 current, 9 graduated):

1. Benjamin Hepner (AFIT, 2024-Present): Computer Vision-enabled Digital Dashboard for T-38 Airplanes
2. Kara Witgen (AFIT, 2024-Present): A Modern Approach to Setting Enlisted Career Field Qualification
   Standards for the United States Air Force
3. Sydney Wekamp (AFIT, 2024-Present): Machine Learning Techniques for Automatic Drift Detection of Sensors in T-38 Airplanes
4. Haley Traub (AFIT, 2024-Present): Machine Learning Techniques to Predict Solar Particle Events and Radiation of Aircrew
5. David Liu (AFIT, 2022-Present): Automated Brain Tumor Segmentation in Medical Imaging Using Machine Learning Algorithms
6. Alex Milham (AFIT, 2023-2024): Predicting and Understanding USAF Pilot Attrition: A Machine Learning Approach to Analyzing Retention Factors
7. Paige Luebbering (AFIT, 2023-2024): Automated Image Registration for Titanium Aircraft Components via Resolution-Robust Parallel Neural Networks
   • Thesis won the MORS Dr. James T. Moore Graduate Research Prize for best application of operations research methodology to a military problem
8. Nolan Skelly (AFIT, 2023): An Improved Saliency Map that Shows Trustworthiness for Localizing Abnormalities in Medical Imaging
   • Poster from Thesis received 1^st Place in the 2023 OR/MS Mini-Poster Competition
9. Matthew Sauer (AFIT, 2023): Federated Active Learning for Network Intrusion Detection
10. Gregory Barry (AFIT, 2022-2023): A Conformal Prediction Approach to Quantify Student Pilot Error via Multimodal Physiological Signals
    • Research featured in AFIT FY22 Graduate School Annual Report and December 2022 AFIT Engineer on Student Outcomes
11. Nathan Johnston (AFIT, 2022-2023): Automated Registration of Polarized Light Microscopy Images Using Deep Learning Techniques.
    • Thesis won the MORS Dr. James T. Moore Graduate Research Prize for best application of operations research methodology to a military problem
12. Byungmoo Kim (AFIT, 2022-2023): Uncertainty Quantification in Federated Learning for Persistent Post-traumatic Headache
13. Grace Metzgar (AFIT, 2022-2023): Inducing Sparsity within High-Dimensional Remote Sensing Modalities for Lightning Prediction
    • Research featured in AFIT FY22 Graduate School Annual Report and December 2022 AFIT Engineer on Student Outcomes
14. Brandon Harvill (AFIT, 2022-2023): Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease; Co-advised with Maj Chancellor Johnstone, Ph.D.

Reader for 10 MS Students (1 current, 9 graduated):

1. Gregory Buchanan (AFIT, 2023-Present): Machine Learning-Based Prediction of Terrestrial Gamma-Ray Flashes: Enhancing Understanding of Atmospheric Phenomena
2. Andrew Gibson (AFIT, 2023-2024): Impact of Operational Ladar Occlusions on Point Cloud Instance Segmentation
3. Connor Oswald (AFIT, 2023-2024): Pilots Who Stay: A Machine Learning Approach to Aircrew Retention
4. Jacob Bryant (AFIT, 2023-2024): Federated Medical Scoring Systems
5. Sung O (AFIT, 2023-2024): Federated Analysis of Wearables Data for United States Air Force Mental and Physical Readiness
6. Dante Reid (AFIT, 2022-2023): Simulation and Analysis of Dynamic Threat Avoidance Routing in an Anti-Access Area Denial (A2AD) Environment
7. Seth Allen (AFIT, 2022-2023): Impact of Drone Autonomy on High Value Airborne Asset Defense Using High Energy Lasers
8. Todd Campo (AFIT, 2022-2023): Prediction of Persistent Post-Traumatic Headache with Long Short-Term Memory Networks
9. Franklin Sun (AFIT, 2022-2023): Analysis and Optimization of Contract Data Schema
10. Nicholas Crino (AFIT, 2022-2023): Garbage In ≠ Garbage Out: Exploring GAN Resilience to Image Training Set Degradations