Course Details
Cognitive Multifunction EMS Warfare: A Machine Intelligence Approach | AOC 2026 Pre-Convention Course
Course Dates: December 7, 2026 | 8 AM – 5 PM
Course Location: Gaylord National Resort & Convention Center | 201 Waterfront St., National Harbor, MD 20745
Course Length: 8 hours total delivered across one course
Description: This unclassified class provides insights and approaches for the development of cognitive multifunction (MF) EMS agents. It is mission-focused and outlines state-of-the-art, machine intelligence-based approaches to achieving mission lethality and resilience under the challenges of complex, dynamic, congested, and contested operational conditions and against advanced threats and cognitive/autonomous adversaries.
Audience: Participation is limited to US citizens only.
This is a technical class; a foundational background in engineering or data science is recommended. Attendees should have a basic understanding of signals and waveforms, general signal processing techniques, radiofrequency (RF) environments, and propagation effects. Additionally, participants should have familiarity with machine learning, and data-driven decision algorithms.
Questions related to the pre-convention course and/or its content can be directed to [email protected].
Participation is limited to US citizens only.
This course will be held before AOC 2026 Convention & Symposium at the Gaylord National Resort & Convention Center. Course registrants must also be registered for AOC 2026.
Learning Objectives
- Awareness of the mission challenges and operational capabilities required to successfully maneuver and fight in the EMS domain.
- An understanding of the architecture and components of MF cognitive EMS agents
- Tools and techniques to leverage deep learning to achieve operational EMS situational understanding (environment, mission context, threat and non-threat behaviors) from received complex IQ samples, learned latent features, and user-defined parameters.
- Insights into multiple methods to increase system autonomy to enhance mission resilience and lethality.
- Critical approaches for grounding MF Cognitive EMS agent development and model training into mission requirements and testing to ensure operational resilience
Agenda
EMS Warfare Challenges (0.5 Hr.)
- Complexity of threats and environment
- Span of missions and mission needs
- Maintaining mission resilience and lethality
Intelligent Agent Architectures for Cognitive Systems (1.5 Hr.)
- Foundation and problem formulation
- Intelligent/autonomous agent architectures
- Functional components and subsystems
- Grounding the agent into mission goals
- Learning from scratch vs. expert system biases
- Online adaptation and continual learning
Achieving Situational Understanding Under Complexity (2.5 Hr.)
- Leveraging deep learning: RFML approaches
- Large-scale IQ data and scenario synthesis
- Multistage signal processing architectures
- Development of HW/SW solution components
Increasing System Autonomy for Mission Resilience and Lethality (2.5 Hr.)
- Control for contextual behavior adaptation
- Reinforcement learning based approaches
- Multifunction action selection
- Adversarial training approaches
- Counter-autonomy/cognitive examples
- Online planning for dynamic adaptation
- In-mission contextual reasoning approaches
Development, Integration, Test (1.0 Hr.)
- New paradigms for requirements and test
- Development of HW and SW solutions
- Continuous capability integration and test
- Role of digital engineering and SecDevOps
- Ensuring mission resiliency through T&E
Registration
Registration will open this summer. The course can be added when registering for AOC 2026.
Questions related to pre-con registration can be directed to Samantha Kim, Professional Development Coordinator, at [email protected].
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AOC Professional Development On-Site Event Cancellation Policy
To cancel a pre-convention course or on-site event registration, submit a written request at least two (2) weeks before the scheduled event via email to [email protected]. Phone cancellations are not accepted. Cancellations received less than two (2) weeks prior to the event will not receive a refund.
- Approved, requested refunds will be issued minus a $100 administrative fee.
- If the registration fee includes materials (e.g., textbooks, printed content) that were pre-purchased or developed before the event, registrants will be invoiced for these items that will include the cost of the material (s), shipping, taxes, and any customs fees, if applicable. Once paid in full, materials will be shipped. Payment must be received within 14 days of the date when the invoice is sent. Invoices not paid within the 14-day window will be subject to additional late fees.
- No refunds will be issued for cancellations made within two weeks of the event or for no-shows.
- If a registered attendee is having trouble obtaining a visa, a written notification must be sent no less than 30 days prior to the course/on-site event to [email protected]. Refunds related to visa obtainment will be evaluated on a case-by-case basis.
Instructor
Sean O’Hara serves as a Technical Fellow and Director of Machine Intelligence and Autonomy at SRC Inc., a not-for-profit serving the defense and intelligence communities. He has 30 years of experience in the development of advanced multifunction RF and cognitive systems. O’Hara brings broad and deep multi-domain subject matter expertise across both machine intelligence technologies and EMS mission areas, including communications, electromagnetic warfare, CEMA, ISR, PNT, NAVWAR, and radar technologies. He has led a dedicated Center of Excellence (COE) for Machine Intelligence and Autonomy at SRC, driving purposeful mission innovation and incubating, accelerating, and advancing state-of-the-art (SOTA) EMS domain capabilities for our mission partners. O’Hara annually teaches Deep Reinforcement Learning for AI as a PhD elective (in its eighth year) at Syracuse University’s Graduate School of Electrical and Computer Engineering. He also teaches an expanded internal version of this class, with participation from SRC government CRADA partners across the Air Force and Army Research Labs and leads numerous EMS domain-specific Radio Frequency Machine Learning (RFML) workshops for these partners to facilitate additional knowledge sharing. Since 2024, hundreds of DOD scientists and engineers have participated in these professional development efforts.
