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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 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.
Academic background
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.
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Collaborations and top research areas from the last five years
Projects
- 1 Active
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CERTIF-AI: CERTIFication of production process quality through Artificial Intelligence
Dijkman, R. M., Dijkman, R. M., Agridag, B., Bukhsh, Z. & Adan, I. J. B. F.
1/11/20 → 31/10/24
Project: Research direct
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A multiobjective decision‐making model for risk‐based maintenance scheduling of railway earthworks
Stipanovic, I., Bukhsh, Z. A., Reale, C. & Gavin, K., 1 Feb 2021, In: Applied Sciences. 11, 3, 18 p., 965.Research output: Contribution to journal › Article › Academic › peer-review
Open Access -
A systematic literature review on requirement prioritization techniques and their empirical evaluation
Bukhsh, F. A., Bukhsh, Z. A. & Daneva, M., Mar 2020, In: Computer Standards & Interfaces. 69, 18 p., 103389.Research output: Contribution to journal › Review article › peer-review
9 Citations (Scopus) -
Maintenance intervention predictions using entity-embedding neural networks
Allah Bukhsh, Z., Stipanovic, I., Saeed, A. & Doree, A. G., Aug 2020, In: Automation in Construction. 116, 18 p., 103202.Research output: Contribution to journal › Article › Academic › peer-review
Open Access1 Citation (Scopus) -
Multi-year maintenance planning framework using multi-attribute utility theory and genetic algorithms
Allah Bukhsh, Z., Stipanovic, I. & Doree, A. G., 9 Jan 2020, In: European Transport Research Review. 12, 1, 13 p., 3.Research output: Contribution to journal › Article › Academic › peer-review
Open Access6 Citations (Scopus) -
Predictive Maintenance for Infrastructure Asset Management
Bukhsh, Z. A. & Stipanovic, I., 1 Sep 2020, In: IT Professional. 22, 5, p. 40-45 6 p., 9194441.Research output: Contribution to journal › Article › Academic › peer-review
1 Citation (Scopus)