System Science and Engineering of Security & Resilience
One of the grand and persistent challenges in computing is the science, engineering, and operation of trustworthy secure systems. Our projects study both the science of security as well as the engineering of secure systems.
- Security of Cyber-Physical Systems and Internet of Things
- Engineering: model-based system and security co-design and analysis of systems and their implementation
- Securing supply chains: provenance and authenticity of engineered products
In all software-integrated system computing and communication platforms play an essential role: they serve as the foundation for architecting complex systems from composition frameworks for interacting components on one hand, and as the isolation layer to shield the higher layers from low-level details on the other hand. Several of our projects deal with the design and engineering of such platforms.
- Cloud and edge computing: highly distributed, systems-of-systems
- Distributed real-time embedded systems: distributed systems operating under timing constraint
- Distributed co-simulation platforms
- Collaboration platforms, tool and data repositories
- Network embedded systems: distributed systems where the boundary and separation between computing and communication architectures disappears, including but not limited to wireless sensor network
- Decentralized systems: systems where functions are completely decentralized, including but not limited to systems based on distributed ledger, blockchain, consensus algorithms, etc.
- Internet of things: systems dynamically formed from ad-hoc networked computing and communication nodes
- North-American Testbed for Time Sensitive Networking (TSN)
Model-Integrated Computing & Design Automation
State-of-the-art systems engineering is model-based: models are used in all phases of systems’ lifecycle. An exciting new research direction focuses on symbiotic design where human-driven model-based design processes are complemented by AI/ML assisted components. Our research covers a broad range of engineering activities where models and data are used both in design and in operations.
- Cyber-physical systems and human cyber-physical systems where humans and computing are tightly integrated into a physical environment
- Design-space exploration, both parametric and combinatorial, with optimization and trade-offs
- Fault diagnostics and prognostics, system health management
- Foundations for Model-integrated Computing / Model-driven design: meta-programmable modeling tools, formal frameworks, domain-specific modeling languages, model transformations, and run-time environments for model-driven system development
- Model integration platforms for physical and biological systems
- Resilient systems that can recover from faults of cyber-effects and continue operating
- Assurance of Cyber-physical systems with learning-enabled components
- Software engineering environments for agile and adaptive system development
- System verification and validation, including both formal and coverage-driven methods
- Large-scale heterogeneous simulation environments for studying complex, emerging behaviors in system-of-systems
Human Cyber-Physical Systems
Human cyber-physical systems where humans and computing are tightly integrated into a physical environment. Software-integrated systems often interact with humans, frequently forming a symbiotic relationship, where the result is more than the sum of its parts. Our projects research the science and engineering of such systems, where humans are assisted by computational systems.
- Human/AI/machine partnerships: education and learning systems where humans and computing systems collaborate in a symbiotic relationship
- Learning and training environments where humans are taught complex knowledge and skills
AI in Engineered Systems
The most exciting new direction in software-integrated systems is the use of AI/ML-based techniques and components, both in design flows, as well as at run-time. These novel approaches offer new opportunities, but generate novel challenges as well. Our projects cover a broad spectrum covering all aspects from design to implementation to operation.
- AI in design flows: system design, synthesis, and discovery using machine learning
- Assured autonomous systems: design and assurance at design-time and run-time
- Learning-enabled systems: architecture, verification, and operation of systems with learning-enabled components
- Learning-enabled, distributed decision making under uncertainty: AI/ML in support of decision making
Societal-Scale Systems and Infrastructure
Modern society is increasingly dependent on software-integrated systems, whose science and engineering is often guided by societal values and preferences. Such systems require the understanding of complex, interacting processes, as well as societal preferences, laws, and policies, such that they operate in a regulated environment, often interacting with a multitude of existing information systems.
- Transportation cyber-physical systems
- Smart cities, smart buildings, connected communities
- Distributed energy systems: microgrids, energy and power management, policy-driven and market-based solutions
- Emergency response systems for communities
- Geographic-scale monitoring of the biome for outbreak prediction and prevention
- Architecture for societal-scale systems that can be parameterized with social context
- Manufacturing systems: monitoring and control, including quality assurance