About Me – Ron Picard

About Me
I am a hyper-driven Senior Engineer, Private Pilot, Amateur (Ham) Radio Operator, and Polymath with a passion for Autonomous Systems, Software Engineering, Air Vehicle Integration, Flight Test, 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 flight test and experimentation programs. I currently Lead & Serve the Department of Defense as a Civilian Engineer for the Air Force Research Laboratory Strategic Development, Planning, and Experimentation (SDPE) office.

Flying Time – Civilian Private Pilot

Aircraft Make & ModelPilot In Command (PIC) HoursFlight Hours
Current As Of 2022-05-17

Flying Time – Flight Test Engineer

Aircraft Make & ModelCrew PositionFlight Hours
RC-135WFlight Test Engineer19.6
NC-135Flight Test Engineer17.0
E-3Flight Test Engineer5.8
Current As Of 2022-12-15


Master of Engineering in Cyber-Physical Systems (Class of 2019)
Wright State University 
GPA: 3.97/4.0

Certified Diploma Download Download

Bachelor of Science in Mechanical Engineering (Class of 2016)
Wright State University 
GPA: 3.506/4.0


Private Pilot – Single Engine Land
Date: 07/07/2021

Amateur (HAM) Radio Technician License
Date: 04/15/2022

General Mobile Radio Service (GMRS) License
Date: 06/01/2020


Experimentation Lead (2017 – Present)
Air Force Research Laboratory (Civilian), Wright Patterson Air Force Base, Ohio
• Leading the state-of-the-art X-62A VISTA (modified F16) modernization effort with USAF Test Pilot School to test and evaluate AI algorithms.
• Transforming the flight test world to evaluate autonomy capabilities on group 5 drones (XQ58, MQ20, UTAP22) with the U.S. Air Force Test Center.• Led a 30+ person engineering team in Autonomy, Software, Hardware, & SIL development; and integration of an autonomy system onto multiple Attritable tactical drones.
• Executed Multiple Sorties Onboard RC-135s & an E-3 to test autonomous aircraft systems.
• Contributed Program Management and Engineering Leadership support to a large air force autonomy program on topics including Autonomy Development, Vehicle Vendor Integration, 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 team through the execution of a QFD architecture evaluation process to select a software baseline autonomy system.
• 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.


Two General Atomics MQ-20 Avengers fly collaborative unmanned aircraft teaming experiments during Edwards Air Force Base’s Orange Flag 21-3 (Photo courtesy of General Atomics)




Software Engineer (2016 – 2017)
SRC Inc.
• 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.

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Samuel J. Heyman Service To America Medal Nomination (#350/950,000) (CY23)
AFRL Commander’s Trophy Nomination (#12/10,000) (CY22)
AFRL Integrated Capabilities Directorate Director’s Trophy (#1/100) (CY22)
AFRL Strategic Development Planning & Experimentation (SDPE) Special Act (#1/1) (Q2CY22)
AFRL Commanders Cup Award (#1/10,000) (CY22)
AFRL/RQQA Special Act Individual Award (#1/1) (Q2CY21)
FLCMC/WA Fighters & Advanced Aircraft Large Team of the Quarter Award (#1/100) (Q1CY21)
AFRL S&T Team Award Nomination (#5/10,000) (AFMC/AFRL) (CY20)
AFRL Aerospace Systems Directorate Director’s Trophy Nomination (#5/120) (AFRL/RQ) (CY20)
AFRL S&E Junior Power & Control Division Quarterly Award (#1/30) (AFRL/RQQ) (Q3CY20)
AFRL 711 HPW/RHCC Dedicated Service Award (#1/1) (Q1CY20)
USAF Palace Acquire Full & Books Scholarship (CY18-CY19)
Dean’s List (CY14-CY15)
NASA Robotic Mining Competition (#4/50) (Q2CY16)
Senior Design Team Honorable Mention (#1/30) (Q2CY16)
Senior Design Showcase Nominee (#10/30) (Q2CY16)
Strivers Alumni Scholarship Award Winner (CY15)
Ohio Means Interns and Co-ops II Scholarship (CY15)
Charles H. Hewitt Scholarship (CY15)


Title: The AAAx Vision to Accelerate Autonomy Test & Evaluation using the X-62A VISTA

Authors: Ronald S. Picard and Matthew R. Niemiec

The Autonomous Attritable Aircraft Experiment (AAAx) is an experimentation campaign that seeks to (1) Determine the competitive advantage of autonomous attributable aircraft to the U.S. Air Force, (2) Demonstrate the operational integration of autonomous attritable aircraft with crewed teammates, and (3) Determine the infrastructure required to test, support, and sustain autonomous attritable aircraft. From Spring 2021 to Spring 2022 AAAx executed 16 autonomous uncrewed vehicle flight tests on multiple group-4/5 drones. During the course of testing, vehicle issues interfered with 5 of 16 flight tests. In addition, since these platforms are uncrewed, additional USAF safety and airworthiness processes resulted in flight test delays. Inorder to overcome vehicle-related delays and USAF process delays caused by testing exclusively with uncrewed vehicles, the AAAx team has invested in the USAF Test Pilot School’s X-62A VISTA to serve as a crewed, tactical surrogate vehicle for testing and evaluating control-based AI algorithms and fixed-wing vehicle models. This paper will present the AAAx team’s vision for leveraging the X-62A VISTA platform to accelerate the test and evaluation of AI/Autonomy capabilities.

View Paper: https://arc.aiaa.org/doi/pdf/10.2514/6.2023-1925
View Video Presentation: https://doi.org/10.2514/6.2023-1925.vid

Title: A Transfer Function for Relating Mean 2D Cross-Section Measurements to Mean 3D Particle Sizes

Authors: A. R. C. Gerlt, R. S. Picard, A. E. Saurber, A. K. Criner, S. L. Semiatin, E. J. Payton

Abstract: It is common practice to estimate the 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 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.

View Paper: https://link.springer.com/article/10.1007/s11661-018-4808-8