Dr. Raz research brings Digital Transformation to complex systems and systems of systems with MBSE, AI/ML, and Information Fusion

My research is focused on Autonomy in System-of-Systems (SoS) and developing Systems Engineering (SE) methods and techniques to enable coordinated missions and new capabilities from large scale distributed and complex systems, particularly for national security purposes. Since joining Mason, I have introduced foundational concepts in Mission Engineering with SoS, developed techniques for safe and reliable integration of autonomy (AI/ML) in systems, and began a SE-based foundation for developing complex systems with digital twins. My research is funded by some of the leading national security research institutions in the nation, such as DARPA, OUSW R&E, AFRL, ONR, NCMS, SERC, and ARL to name a few. Since joining Mason, I has participated in $15.3Mil in externally funded research, with PI/Co-PI share of ~$5.6Mil.

Digital Transformation and Model-based Systems Engineering

Dr. Raz’s work advances Digital Transformation for complex systems by integrating Model‑Based Systems Engineering (MBSE) across the full system lifecycle. His research develops rigorous modeling frameworks, digital twins, and data‑driven engineering methods that improve traceability, verification, and decision‑making for large‑scale, distributed systems. These approaches enable faster iteration, reduced integration risk, and more resilient mission‑focused system designs.

System-of-Systems, Mission Engineering, and Command-and-Control Systems

In the domain of Command‑and‑Control (C2) and System‑of‑Systems (SoS) engineering, Dr. Raz focuses on enabling coordinated missions across heterogeneous, distributed assets. His work introduces foundational concepts in mission engineering, intent‑driven coordination, and dynamic reconfiguration of complex systems. These methods support more adaptive, resilient, and scalable C2 architectures capable of operating in contested and rapidly evolving environments.

Counter-Drone, AI/ML, and Information Fusion

Dr. Raz develops advanced AI/ML, autonomy integration, and information fusion techniques to support emerging challenges such as counter‑UAS operations and multi‑sensor situational awareness. His research includes high‑level data fusion architectures, safe and reliable integration of autonomy, and AI‑enabled decision support for national‑security missions. These capabilities enhance detection, tracking, and response effectiveness in complex operational settings.

Research Projects

DELTA-FORCE: Digital Engineering for Lifecycle Test and Analysis For Off-Road Combat Vehicles

DELTA-FORCE aims to develop a comprehensive digital engineering methodology for new vehicle concepts.

Intent-Based Networking for Distributed Command and Control (IBODC2)

This project leverages the emerging concepts and principles from Intent-based Networking (IBN) in software-defined networks for dynamically creating multiple pathways to achieve CI with distributed complex systems.

MUSCAT: Mason’s UAV Systems Cyber Analysis Testbed

A system of systems modeling and simulation testbed for sensing, tracking, and fusion of sUAV to guide infrastructure-level decision making.

Digital Engineering Modeling of Integrated Photonics: Free Space Optical Communication System (FSOC)

This project is developing Digital Engineering solutions for photonics-based free-space optics communication technology.

AI and Advanced Analytics System of Systems and Kill Chain to Kill Web Analysis

This project underpins war targeted at blocking strategy of hitting U.S. information nodes, and the technologies necessary to accomplish that force design.

Novel Hypersonic Vehicle Maneuvers Via Reinforcement Learning Techniques

Reinforcement Learning (RL) enables training of an artificial intelligence (AI) agent to operate in dynamic uncertain environments.

Systems Engineering Architecture for Recombinant AI

The System Architecture for Recombinant AI (SARAI) task structure provides an opportunity to advance the state of the art in Systems Engineering for AI (SE4AI) by enabling quick prototyping, combination, and reuse of data and AI software assets.

Update to the Department of Defense Systems Engineering Guide for Systems of Systems

This project integrated the latest research and state-of-the-art standards to ensure the SoSE guidbook remains relevant and highly useful for the defense workforce.

High Level Data Fusion (HLDF) System Architecture for Counter Unmanned Aerial Systems (CUAS)

Develops a Model-based Reference Architecture for High-level Data Fusion (HLDF) for Counter Drone Operations.

Digital Engineering Tools for Acquisition Support: A Systematic Approach for Autonomous System Acquisition and Integration into SoS via Data-Driven Trade Studies

This project uses a System Architecture for Recombinant AI (SARAI) task structure provides an opportunity to advance the state of the art in Systems Engineering for AI (SE4AI) by enabling quick prototyping, combination, and reuse of data and AI software assets.

A Biologically inspired, Lighter-than-air, Instructional, Mechatronics Program (BLIMP)

An educational system designed to teach students about system attributes, functional flow, and system integration through hands-on learning.