Zaharah Bukhsh
Papers
2
Total Citations
41
H-Index
2
About
Zaharah Bukhsh is a leading researcher at the intersection of reinforcement learning (RL) and industrial optimization, with a particular focus on bridging the gap between theoretical AI advances and high-impact real-world applications. Her work addresses critical challenges in smart industry and asset management, where she has pioneered the use of RL for optimal maintenance planning—a domain where traditional AI applications remain scarce. Her most-cited paper (39 citations) from the 14th International Conference on Agents and Artificial Intelligence (2022) demonstrates how RL can be effectively deployed for complex industrial decision-making, moving beyond robotics and games into practical, high-stakes environments. More recently, Bukhsh has broken new ground in collaborative human-robot systems, developing uncertainty-aware policies that ensure both efficiency and fairness in warehouse order picking. Her 2024 paper on this topic introduces novel optimization approaches for allocating human pickers to Autonomous Mobile Robots (AMRs), addressing the critical challenge of balancing productivity with equitable workload distribution. Through her work, Bukhsh is helping to shape the future of intelligent automation, making AI systems that are not only powerful but also practical and socially responsible for industrial deployment.
Research Focus
Key Achievements
Top Papers
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