A hands-on, applied program equipping aerospace professionals to identify, design, secure, and deploy practical AI solutions in safety-critical environments — no coding required.
Most AI training programs start with algorithms. This one starts with problems worth solving. The specialization is built around a problem-first philosophy — because the hardest challenge in aerospace AI is not building a model, it is knowing which problem to target, which constraints to respect, and which failure modes to design against before a single tool is selected.
Across 4 courses and 72 hours of applied instruction, participants move from problem identification and AI fundamentals through structured execution, secure architecture, and local deployment — culminating in a capstone project that produces a deployable, mission-relevant AI system design. Every module is grounded in the realities of aerospace operations: regulatory compliance, safety-critical decision-making, and adversarial threat environments.
4 Courses / Sequentially structured for progressive depth
72 Hours / Applied, hands-on instruction time
No Coding / Accessible to non-technical professionals
Capstone / Produce a deployable AI system design
The specialization is structured as a deliberate progression — each course builds directly on the previous, moving participants from conceptual fluency to technical confidence to operational deployment readiness.
Learn to distinguish problems AI can solve from those it cannot. Build a working vocabulary of AI concepts — supervised learning, classification, anomaly detection — anchored to real aerospace use cases. Master the problem framing process that drives every downstream decision.
Translate identified problems into structured AI project plans. Develop requirements, select appropriate model types, define success metrics, and build execution frameworks aligned to aerospace program management practices. Apply design thinking at every decision gate.
Design AI architectures that operate in air-gapped and restricted environments. Understand adversarial threats, model integrity, and data pipeline security. Learn to deploy AI locally without cloud dependency — a critical capability for classified and mission-sensitive programs.
Integrate all prior coursework into a complete AI system design document. Address problem framing, architecture decisions, security posture, deployment constraints, and stakeholder communication. Graduate with a portfolio-ready artifact demonstrating mission-relevant AI competency.
The specialization is not a survey of AI tools. It is a structured methodology. Two proprietary frameworks provide the intellectual scaffolding for the entire program — ensuring that every design choice is defensible, every deployment is appropriate, and every outcome is aligned to mission requirements.
Adapted from human-centered design methodology and reengineered for high-stakes aerospace contexts. This framework guides participants through five disciplined phases: empathize with the operational environment, define the problem with precision, ideate within safety constraints, prototype with appropriate tools, and test against failure-mode criteria.
The framework explicitly accounts for regulatory compliance, human-machine teaming, and the asymmetric consequences of AI errors in safety-critical systems — where false negatives can be mission-ending.
A fundamental distinction that most practitioners overlook. Descriptive AI tells you what happened — it surfaces patterns in historical data for situational awareness. Prescriptive AI tells you what to do — it generates recommendations or autonomous actions in operational contexts.
Knowing which type of AI is appropriate for a given aerospace use case is not a technical question. It is a design and governance question — one that determines accountability structures, certification pathways, and operator trust. This framework ensures participants make that distinction deliberately, every time.
This specialization requires no programming background. It requires professional judgment, domain expertise, and the willingness to apply both in new ways. If your work touches safety-critical systems, operational decisions, or program outcomes — this program was built for you.ways. If your work touches safety-critical systems, operational decisions, or program outcomes — this program was built for you.
Apply AI to failure mode analysis, predictive maintenance, and design verification. Learn to specify AI requirements with the same rigor you apply to hardware and software components.
Understand how AI intersects with AS9100, DO-178C, and MIL-SPEC frameworks. Develop the vocabulary to evaluate AI system documentation, audit AI-assisted processes, and maintain certification traceability.
Make informed build-versus-buy decisions, scope AI integrations realistically, and communicate AI program risks to stakeholders. Leave with frameworks for AI program governance and milestone definition.
Apply AI to demand forecasting, supplier risk scoring, and counterfeit parts detection. Understand the data requirements, vendor evaluation criteria, and integration risks specific to aerospace supply networks.
Leverage AI for operational decision support, anomaly triage, and resource optimization — in environments where latency, reliability, and explainability are non-negotiable requirements.
Module ownership is aligned to the program arc: safety-critical foundations in Module 1, followed by workflow design, secure architecture, and capstone execution in Modules 2–4.
Module 1: Problem Identification & AI Foundations for Safety-Critical Environments
Jamal Madni is a technology leader and aerospace professional based in Los Angeles, CA. He holds an Applied Data Science Postgraduate Program certification from MIT and has held senior leadership roles at Boeing, including Chair of Boeing's Network & Space Emerging Leadership Development Program (ELDP) and Technology Ambassador for Boeing. His ELDP program develops future leaders across Boeing's global network — spanning ~15,000 employees across 35 states and 12 countries, with a select cohort of 135 top performers. He brings deep operational and data science expertise to the problem-first foundations of the specialization.
"The hardest challenge in aerospace AI isn't writing the code. It's knowing exactly which safety-critical problem to solve, which failure modes to anticipate, and when to keep the human in the loop."
Modules 2, 3, and 4: structured AI workflow design, secure tool stacking and deployment strategy, and the final pilot-ready aerospace AI capstone.
Antonio Pagano operates at the intersection of law, technology strategy, and aerospace operations. With graduate credentials in both law and business administration, he brings a rare interdisciplinary perspective to AI education — one that takes seriously the legal and regulatory exposure created by AI system failures in safety-critical environments. As an instructor at UCLA Extension, he developed curriculum that bridges the gap between theoretical AI concepts and the operational realities faced by aerospace professionals. His teaching methodology is problem-first, tool-second.
"The goal is not to make aerospace engineers into data scientists. The goal is to make them dangerous with tools — precise, safe, and effective."
Whether you are evaluating the program for your team, exploring individual enrollment, or seeking to discuss a customized delivery for your organization — reach out directly. All inquiries are handled personally by the instructor.
Join the next cohort as an individual professional. Bring your own aerospace use case and leave with a deployable design artifact.
Custom delivery options available for aerospace contractors, government agencies, and program offices. Curriculum can be tailored to mission-specific AI use cases.
Questions about prerequisites, enrollment logistics, or the UCLA Extension curriculum scope? Get direct support regarding the course framework—ensuring you have a clear path from registration to completion.