- Advance your expertise with this essential course, focusing on responsibly building systems with Artificial Intelligence and using AI to enhance the systems engineering and deployment processes, all the while understanding and managing risks.
- All students will receive an AIAA Certificate of Completion at the end of the course.
OVERVIEW
This course is tailored to equip aerospace professionals with the essential knowledge, skills, and analytical abilities to tackle the challenges of responsibly designing and deploying AI-integrated systems. As AI becomes increasingly embedded in aerospace, it presents significant opportunities for efficiency, cost reduction, and safety enhancement. However, recognizing and mitigating the associated risks is essential to ensure the safety, reliability, and ethical integrity of these technologies.
Participants will gain a solid foundation in AI fundamentals, learn how to architect AI systems, understand the principles of systems engineering as they apply to AI, understand and evaluate ethical and societal implications, and manage AI risk. Upon completing this course, students will be well-prepared to develop and manage complex systems with embedded AI, including identifying unique requirements, testing, and certifying these systems, and maintaining safe performance levels. This is particularly crucial for safety-critical systems, such as self-flying vehicles and drone delivery, where rigorous testing, evaluation, monitoring, and maintenance is paramount.
This course provides a foundation for systems engineers to understand the implications of both building systems with Artificial Intelligence (SE for AI) and using Artificial Intelligence to enhance the systems engineering process (AI for SE). The course introduces the foundations of AI, including different types of machine learning, and the associated design, test, and evaluation challenges for AI systems. AI opportunities for transforming SE lifecycle activities are discussed along with applications of AI in modern systems.The course will then delve into Responsible AI (RAI) principles to include pressing ethical, societal, and policy issues, such as transparency, trust, safety, and security.
Lastly, this course will explore the fundamental issues that underpin risk inherent in aerospace systems that utilize AI. Students will learn how to measure these risks, assess the impacts and harms that could result from AI, and formulate plans for managing risks including testing, maintenance, governance, and legal interventions. Topics will include AI robustness, generalizability, validity, reliability, safety, and security.All of these topics will be examined through specific aerospace use cases and current events, providing a grounded understanding of the challenges and considerations in autonomy and AI for the aerospace sector.
LEARNING OBJECTIVES
- Deepen understanding of AI Foundations and AI Intensive Systems: Develop a foundation in artificial intelligence principles, including various types of machine learning, and how they apply to aerospace systems.
- Define the Requirements and Risk for AI Systems: Applying strong systems engineering to specify and assess the implementation of AI in larger systems.
- Explore the Challenges of Fielding AI Systems: Using systems engineering to inform Ai Component Definition and Integration, Testing, Verification, and Validation
- Examine AI in Aerospace Opportunity: Survey the wide breadth and possibility space for AI systems in aerospace applications.
- Understand Core Responsible AI (RAI) Principles: Gain a thorough understanding of the foundational principles of Responsible AI, including transparency, fairness, and accountability.
- Understand RAI Frameworks: Explore various frameworks and methodologies for implementing Responsible AI, tailored to address ethical regulatory, and policy considerations.
- Understand AI Risk Management: Measuring, assessing, and mitigating the inherent risks in AI systems, from safety and security to governance and legal interventions.
- Apply RAI to Aerospace Use Cases: Apply Responsible AI principles to real-world aerospace scenarios, addressing practical challenges and ensuring ethical and effective AI integration.
- [Detailed Outline below]
AUDIENCE: This course is designed for professionals tasked with driving the safe and effective integration of AI within their organizations, who are eager to enhance their expertise in the design, testing, and deployment of cutting-edge AI-based aerospace technologies.
COURSE FEES (Sign-In to Register)
- AIAA Member Price: $945 USD
- AIAA Student Member Price: $595 USD
- Non-Member Price: $1,145 USD
Recommended AI Course:
Generative AI for Code Generation and Evaluation: From HumanEval to AeroEval – Online Short Course (Starts 9 November 2026)
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OUTLINE|
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Class 6 Dr. Phil Barry |
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INSTRUCTORS
Dr. Phil Barry is a strategic and technical leader with a robust background in systems engineering, military simulation, and space mission-critical systems. As the Director of the Mission and Systems Solutions Group at the L3Harris Corporation, he leads future architecture definition, advanced concept refinement, and modeling, simulation, and analysis projects to deliver key capabilities for the DoD and the Intelligence Community. Dr. Barry is also an Adjunct Professor at George Mason University where he teaches courses in the Systems Engineering of Artificial Intelligence, Heterogeneous Data Fusion, Decision Analysis, and Project Management. He has a Ph.D. in Information Technology, an MS in Systems Engineering, and a BS in Aerospace Engineering.
Dr. Jesse Kirkpatrick is a Research Associate Professor and the co-director of the Mason Autonomy and Robotics Center at George Mason University. Jesse is also an International Security Fellow at New America and serves as a consultant for numerous organizations, including some of the world’s largest technology companies. Dr. Kirkpatrick’s research and teaching focuses on responsible innovation, with an emphasis on Responsible AI. He has received numerous honors and awards and is an official “Mad Scientist” for the U.S. Army.
Mr. Sri Krishnamurthy, CFA, CAP, is the founder of QuantUniversity. With over twenty years of experience, Sri has guided and consulted with various organizations in AI, Quantitative Analysis, Risk Management, Fintech, Machine Learning, and Statistical Modeling related topics. Previously, Sri has worked for Citigroup, Endeca, and MathWorks, with extensive consulting roles for numerous top-tier clients. Sri has guided over 5,000 students and professionals through intricate quantitative methods, analytics, AI, and big data topics in the industry and as a faculty member at George Mason University, Babson College, and Northeastern University. Sri is a recognized thought leader and is a frequent speaker at multiple CFA, PRMIA, QWAFAFEW, TEDx events and at various international finance and machine learning conferences.
CLASSROOM HOURS / CEUs: 16 classroom hours / 1.6 CEU/PDH
Cancellation Policy: On-demand purchases are non-refundable.
Contact: Please contact Lisa Le or Customer Service if you have any questions about the course or group discounts.
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