Lei Lei

University of Guelph, Rutgers, The State University of New Jersey

Papers

Total Citations

244

H-Index

About

Lei Lei is a pioneering researcher whose work bridges the fields of robotics, optimization, and artificial intelligence. Her key research areas include robotic scheduling, production optimization, and federated reinforcement learning. Lei’s major contributions are twofold: she developed effective optimization algorithms for cyclic scheduling in multi-stage robotic processing lines with time-window constraints, solving a classic hoist scheduling problem that had long challenged the field. Her foundational work in this area, published in 2001, remains influential with 45 citations. More recently, Lei has made a significant impact in the emerging field of federated reinforcement learning, authoring a comprehensive 2021 survey that has already garnered 199 citations. This work explores techniques, applications, and open challenges, positioning her as a leading voice in the intersection of distributed machine learning and robotics. Her research is notable for its practical focus on real-world manufacturing and automation problems, and her ability to bridge classical optimization with modern AI approaches. Lei’s work continues to inspire researchers in both industrial engineering and intelligent systems, demonstrating lasting relevance across two decades of scholarship.