Contactz.bukhsh@ tue.nl Atlas 5.323
Zaharah A. Bukhsh is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e). Her research interests lie in the area of data-driven decision support technologies, mainly artificial Intelligence, interpretable data modeling, deep reinforcement learning, and decision theory methods. She focuses on developing learning-based decision support methods and tools to improve, solve and optimize operational challeneges, including resource utilization, cost optimization, planning, and scheduling. Her research interest also extends to the area of process mining to monitor and improve the operational processes.
Zaharah received her MSc (2015) in Information Systems Engineering and her PhD in Operations research (2019) from the University of Twente. Her PhD research was about developing data-driven decision support methods for the optimal maintenance planning of transport infrastructure. She has collaborated nationally and internationally with industry, e.g., Rijkswaterstaat, IrishRail, etc., and academic partners, e.g., ETH, Trinity College Dublin, and TU Delft. Her PhD project was funded by the H2020 research project DESTination RAIL and COST ACTION TU1406. Before joining TU/e in 2021, she contributed to the PrimaVera (NWO) project as a post-doc researcher at the Software Science department of Radboud University.
Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective OptimizationIEEE Symposium Series on Computational Intelligence, IEEE SSCI 2022 (2023)
Operator Selection in Adaptive Large Neighborhood Search using Deep Reinforcement LearningarXiv (2022)
Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks14th International Conference on Agents and Artificial Intelligence, ICAART 2022 (2022)
Damage detection using in-domain and cross-domain transfer learningNeural Computing and Applications (2021)
A multiobjective decision‐making model for risk‐based maintenance scheduling of railway earthworksApplied Sciences (2021)
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