Published in Nature Communications, 2025
Probabilistic framework identifying dominant drivers of long-term decarbonization uncertainty.
Recommended citation: Khayambashi, K., Clarens, A. F., Shobe, W. M., & Alemazkoor, N. (2025). Identifying key uncertainties in energy transitions: A Puerto Rico case study. Nature Communications, 16(1), 9064.
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Published in Reliability Engineering & System Safety (Under Review), 2025
Topology-aware multi-agent reinforcement learning framework for scalable post-hurricane power distribution restoration.
Recommended citation: Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2025). GNN-based multi-agent reinforcement learning for power distribution recovery. Reliability Engineering & System Safety.
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Published in Joule (Under Review), 2025
This study quantifies the system-level interaction between accelerating data center (IDC) electricity demand and residential heat pump (HP) adoption in Virginia’s PJM Dominion (PJMD) region through 2040.
Recommended citation: Khayambashi, K., Kaufman, M., DeCarolis, J., Shobe, W., Wade, C., McCollum, D., Alemazkoor, N., & Clarens, A. F. (2025). Interactions Between Data Center Load Growth, Residential Heat Pump Adoption, and Energy Affordability. Joule (Under Review).
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Published in Engineering Applications of Artificial Intelligence, 2025
This paper provides a systematic and structured review of Graph Neural Network (GNN) applications across foundational civil infrastructure domains, including transportation networks, power systems, water distribution systems, and structural systems.
Recommended citation: Anand, H., Khayambashi, K., Zandsalimi, Z., Taghizadeh, M., Hasnat, M. A., & Alemazkoor, N. (2025). Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems. Engineering Applications of Artificial Intelligence (Under Review).
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Published in IEEE International Conference on Big Data, 2024
GNN-based prediction framework enabling direct control over decompression error for large-scale spatio-temporal data.
Recommended citation: Khayambashi, K., & Alemazkoor, N. (2024). Graph neural networks for precision-guaranteed compression of large-scale data. Proceedings of the IEEE International Conference on Big Data.
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Published in Electric Power Systems Research, 2024
This paper introduces a residual multi-fidelity graph neural network (MF-GNN) framework for fast and accurate probabilistic power flow analysis in modern grids with high renewable penetration and stochastic load variation.
Recommended citation: Taghizadeh, M., Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024). Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty. Electric Power Systems Research, 237, 111014.
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Published in Journal of Machine Learning for Modeling and Computing, 2024
Published in Journal of Machine Learning for Modeling and Computing (2024).
Recommended citation: Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024). Hybrid chance-constrained optimal power flow under load and renewable generation uncertainty using enhanced multi-fidelity graph neural networks. Journal of Machine Learning for Modeling and Computing, 5(4), 53–76.
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Published in Sustainability, 2023
This study integrates experimental pavement engineering with machine learning–based predictive modeling to identify optimal compositions of reclaimed asphalt pavement (RAP), crumb rubber (CR), waste engine oil (WEO), and steel slag (SS) under balanced mix design constraints.
Recommended citation: Khorshidi, M., Goli, A., Orešković, M., Khayambashi, K., & Ameri, M. (2023). Performance evaluation of asphalt mixtures containing different proportions of alternative materials. Sustainability, 15(18), 13314.
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