Zaharah Bukhsh
Department / Institute
![](https://assets.w3.tue.nl/w/fileadmin/_processed_/c/7/csm_80091baa401d54efc411c4e1076b734a_49fe052993.jpg)
RESEARCH PROFILE
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 deep reinforcement learning. She focuses on developing learning-based optimization methods and tools to improve, solve, and optimize operational challenges, including resource utilization, cost optimization, planning, and scheduling. Her research interest also extends to the area of process mining to monitor and improve operational processes.
ACADEMIC BACKGROUND
Zaharah earned her MSc (2015) in Information Systems Engineering and her PhD in Operations Research (2019) from the University of Twente. Her PhD research focused on developing data-driven decision support methods for the optimal maintenance planning of transport infrastructure. Her PhD research work was characterized by a strong collaborative spirit, having partnered with (inter)national industry leaders, such as Rijkswaterstaat and IrishRail, and renowned academic institutions, including ETH Zurich, Trinity College Dublin, and TU Delft. The H2020 research projects DESTination RAIL and COST ACTION TU1406 funded her PhD project. Before joining TU/e in 2021, she contributed to the PrimaVera (NWO) project as a post-doc researcher at the Software Science Department, Radboud University.
Recent Publications
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Graph Neural Networks for Job Shop Scheduling Problems
(2024) -
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
(2024) -
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
(2024) -
Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems
(2023) -
On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
(2023)
Ancillary Activities
No ancillary activities