Research Areas: Difference between revisions
Created page with "== Research Areas == Our work on system software unfolds along three intertwined directions. === 🧠 AI & Intelligent Software === Bringing AI to the heart of system software. <div style="display:flex; flex-wrap:wrap; gap:15px; margin:15px 0;"> <div style="flex:1 1 280px; padding:20px; background:#faf5ff; border-left:4px solid #8a3aa8; border-radius:6px;"> <div style="font-size:115%; font-weight:bold; color:#4a1a6c; margin-bottom:8px;"> 🤖 AI-Driven System Softwa..." |
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Latest revision as of 21:39, 3 May 2026
Research Areas
Our work on system software unfolds along three intertwined directions.
🧠 AI & Intelligent Software
Bringing AI to the heart of system software.
🤖 AI-Driven System Software
Embedding AI into the system software stack to enable self-adaptive, self-diagnostic, and intelligent decision-making systems. This includes applying Vision-Language Models (VLM) and Agentic AI at the system level.
🦾 Physical AI
Designing and verifying AI systems that interact directly with the physical world through sensors and actuators — ensuring that intelligence operates safely and predictably in autonomous platforms.
✍️ LLM for Software Engineering
Leveraging Large Language Models to transform software engineering workflows — from requirements analysis and code generation to automated documentation of safety-critical software.
🛡️ Reliability & Assurance
Verification and monitoring techniques that guarantee safety and reliability.
🛡️ Runtime Assurance (RTA)
Designing runtime monitoring and fail-safe mechanisms that guarantee safety of aerospace and autonomous systems during operation, with verification methodologies aligned with avionics standards such as DO-178C.
🔗 Multi-Agent Systems (MAS)
Algorithms for coordination and cooperation among multiple autonomous agents, along with verification methods for distributed systems — ensuring both safety and mission success when multiple unmanned assets operate together.
🏗️ Structural Health Monitoring (SHM)
Sensor-driven techniques for diagnosing and predicting structural conditions, with applications in aircraft structural health monitoring — including AI-based anomaly detection and predictive maintenance.
⚙️ Systems & Infrastructure
The foundational layer — system software infrastructure and distributed computing.
🛰️ Digital Twin (DT)
Building digital twin models with real-time synchronization for aircraft, UAVs, and autonomous systems — enabling monitoring, predictive diagnostics, and simulation-driven decision-making.
☁️ Edge Computing & Computation Offloading
Optimizing the distribution of computation between resource-constrained edge devices and cloud/server backends — balancing latency, power, and communication costs in distributed computing architectures.
✈️ UAV & Airborne Software
Designing and verifying embedded software for unmanned aerial vehicles — from flight control to mission management — including work on open platforms such as PX4 and ROS2 and applications of avionics software standards.