System Level

System-level research focuses on understanding and improving the performance and reliability of complex systems, particularly in the context of artificial intelligence and robotics. Current efforts concentrate on analyzing the impact of non-ideal hardware components (like memory in neural networks) and environmental factors (like out-of-distribution data in autonomous driving) on overall system behavior, often employing simulation-based verification and validation techniques. This work is crucial for ensuring the safety and robustness of AI-powered systems in real-world applications, ranging from autonomous vehicles to robots operating in unpredictable environments.