I am a hyper-driven Pilot & Engineer with a passion for Software Engineering, Air Vehicle Integration, and Aviation. I have a demonstrated history of Leading Technology Development, Integration, and Flight Test Execution to build real solutions that directly impact the USAF’s Air Superiority via both hands-on and leadership roles within large-scale DoD programs. I currently Lead & Serve the Department of Defense as a Civilian Engineer for the Air Force Research Laboratory.
Flying Resume – Civilian Private Pilot
|Aircraft Make & Model||Pilot In Command (PIC) Hours||Flight Hours|
Flying Resume – USAF Maintenance/Engineering Support Personnel (MESP)
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Bachelor of Science in Mechanical Engineering (Class of 2016)
Wright State University
Private Pilot – Single Engine Land
Private Pilot Certification Course (Part 141)
Ground School Certificate (Part 141)
General Mobile Radio Service (GMRS) License
Student Pilot Certification
DAWIA – Level 1 ADPD Engineering
DAWIA – Level 1 ADPD Science & Technology Management (STM)
Experimentation Lead (2017 – Present)
Air Force Research Laboratory (Civilian), Wright Patterson Air Force Base, Ohio
• Executed Numerous Flight Test Sorties Onboard an RC-135W for the Skyborg Autonomous Attritable Aircraft Experiment (AAAx). Test operator for airborne Command and control station. Monitored a group 5 autonomous target drone via a live link during test point execution. Commanded a simulated autonomous group 5 target drone via a live link to fly specific routes, speeds, headings, & altitudes to accomplish flight test objectives. Solved HW/SW issues real-time during FT execution by debugging comms network problems & SW/GPS timing issues while on the ground & on board a RC-135W.
• Executed Numerous flight tests (FT) under the USAF SKYBORG Vanguard Program & used the data acquired from FT to generate new autonomous drone (AD), manned fighter, & C2 aircraft teaming requirements.
• Led a 30+ person Skyborg System Design Agent team in Autonomy, Software, Hardware, & SIL development; and integration of the Skyborg Autonomy Core System (ACS) onto multiple Attritable air vehicles.
• Contributed Program Management and Engineering Leadership support to the USAF Skyborg Vanguard program on topics including: ACS Development, Vehicle Vendor Integration Requirements, Flight Test Support, Program Strategy, Security Guidance, Cloud Infrastructure Development, COMMs integration, Auto-Pilot Integration, Sensor Integration, Run-Time Assurance, and HSI.
• Led a 15-person Skyborg System Design Agent team through the execution of a QFD architecture evaluation process to select a software baseline for the Skyborg Autonomy Core System (ACS).
• Contributed engineering support to a large OUSD(R&E) program through Software Development Kit evaluations, Architecture Metrics, and Programmatic Strategy.
• Technical advisor and scrum master for interns who developed a virtual reality tablet training simulator for USAFSAM CCAT students.
Software Engineer (2016 – 2017)
• Led a research effort on machine learning that focused on exploring frameworks capable of supervised neural networks for autonomous classification of signals.
• Followed an agile, team-based development process to develop waveform pattern visualization and analysis tools to meet the requirements of the customer.
• Developed software using an agile development process utilizing sprints and spirals with scrums and retrospectives.
• Implemented model view controller and model view view-model architectures, along with object-oriented design patterns such as observer, singleton, and factory.
• Put in place a verification and validation framework for a real-time radar simulation engine.
• Wrote automation scripts for Sparx’s UML modeling software, Enterprise Architect, to decrease model design-time.
• Technical ambassador for a new employee.
Materials Student Researcher (2015 – 2016)
Air Force Research Laboratory (SOCHE Contractor)
• Designed a lognormal sphere slicing simulation in MATLAB for a stereological analysis of gamma-prime distributions.
• Created a sphere simulation in MATLAB that calculated the maximum volume fraction of hexagonally close-packed spheres in a cube.
• Performed metallographic characterization of materials used in an experimental rotary detonation engine utilizing scanning electron microscopy techniques.
Mechanical Engineering Co-op (2014 – 2015)
Ferco Aerospace Group
• Programmed a VB.NET module that converted 15,000 Harvard Graphics 3.0 presentation files into Initial Graphics Exchange Specification files in approximately four hours, saving weeks of work and over $5,000.
• Experimentally determined the correct stress concentration factor needed for the air bending of aerospace brackets by designing and fabricating test parts out of sheet metal.
• Designed 3-D Unigraphics models using 2-D AutoCAD files.
2020 AFRL S&T Team Annual Award Nomination (AFLCMC/WA) (Q1CY21)
Fighters & Advanced Aircraft Large Team of the Quarter Award (AFLCMC/WA) (Q1CY21)
AFRL S&T Team Award Nomination (AFMC/AFRL) (CY20)
Director’s Trophy Nomination (AFRL/RQ) (CY20)
S&E Junior Power & Control Division Quarterly Award (AFRL/RQQ) (Q3CY20)
711 HPW/RHCC Dedicated Service Award (Q1CY20)
Palace Acquire Full & Books Scholarship (CY18-CY19)
Dean’s List (CY14-CY15)
NASA Robotic Mining Competition (#4/50) (Q2CY16)
Senior Design Team Honorable Mention (Q2CY16)
Senior Design Showcase Nominee (Q2CY16)
Strivers Alumni Scholarship Award Winner (CY15)
OH Means Interns and Co-ops II Scholarship (CY15)
Charles H. Hewitt Scholarship (CY15)
A Transfer Function for Relating Mean 2D Cross-Section Measurements to Mean 3D Particle Sizes
A. R. C. Gerlt, R. S. Picard, A. E. Saurber, A. K. Criner, S. L. Semiatin, E. J. Payton
It is common practice to estimate mean 3D particle and grain size of polycrystalline materials by multiplying 2D cross-sectional measurements by a multiplication factor. However, the most frequently used multiplication factors apply only to uniform or specific dispersions of particles, and therefore can provide misleading results. In the present work, empirical equations are developed to more accurately predict the mean 3D grain size of a lognormal spherical particle dispersion, regardless of the dispersion’s width. The equations provide an improvement over scalar multiplier values by allowing the effects of particle size distribution to be accounted for using inputs that can be obtained by cross-sectional analysis.