Lui Sha

University of Illinois System, University of Illinois Urbana-Champaign

Papers

Total Citations

170

H-Index

About

Lui Sha is a prominent researcher whose work spans real-time control systems, scheduling theory, and safety-critical cyber-physical systems. His most influential contributions center on adaptive and elastic approaches to real-time computing, particularly addressing the tension between fixed scheduling assumptions and the dynamic demands of modern robotic and control applications. His 2002 papers on elastic feedback control and handling execution overruns pioneered techniques for managing task periods and worst-case execution times more flexibly, enabling more robust and responsive hard real-time systems — work that has collectively garnered over 150 citations and remains foundational in embedded systems research. His online control optimization framework further demonstrated how load-driven scheduling could improve system performance dynamically rather than relying on static, conservative configurations. Beyond scheduling, Sha has contributed to educational technology through the RemoteLab tele-laboratory environment, expanding access to hands-on engineering experimentation. More recently, his 2025 work on Runtime Learning Machines reflects a forward-looking agenda, bridging high-performance machine learning with high-assurance guarantees for safety-critical cyber-physical systems — a timely and increasingly vital research direction as AI becomes embedded in mission-critical infrastructure.