Analytical review synthesizing climate change impacts on power systems, integrating demand growth, infrastructure degradation, extreme events, uncertainty modeling, and renewable transition planning.
Graph Neural Network–based reinforcement learning framework for integrating hyperscale data centers into post-disaster distribution system restoration.
Capacity expansion modeling framework quantifying interactions between hyperscale data center growth, residential electrification, and long-term energy affordability.
Probabilistic energy transition planning framework integrating capacity expansion modeling, hierarchical Monte Carlo simulation, and machine learning surrogates.
Probabilistic integrated assessment modeling framework coupling carbon dioxide removal deployment with power-sector evolution under deep uncertainty.
Graph neural network framework for prediction-based compression with explicit decompression error guarantees across large-scale spatio-temporal datasets.
Systematic review of Graph Neural Network methodologies across transportation, power, water, and structural infrastructure systems, identifying research gaps and future directions.
Surrogate-accelerated chance-constrained optimal power flow framework combining multi-fidelity graph neural networks with hybrid constraint validation under renewable and load uncertainty.
Topology-aware multi-agent reinforcement learning framework for scalable power distribution system restoration after hurricanes.
Geospatial risk-based decision-support system for statewide prioritization of stormwater infrastructure inspection and maintenance under climate stressors.
Residual multi-fidelity graph neural network framework enabling scalable probabilistic power flow analysis under high-dimensional renewable and load uncertainty.
Regression-based machine learning framework for predicting rutting, cracking, and moisture resistance of asphalt mixtures and identifying feasible design regions under traffic-level constraints.
Utility-scale graph learning framework for structure-level failure localization and cause prediction across large transmission networks.
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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Published in Advancing the Resilience of the Power Grid under a Changing Climate (IEEE & Wiley), 2024
Integrated review of climate uncertainty, resilience, and renewable transition challenges in long-term power grid planning.
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 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 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 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 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 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 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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Graduate Teaching Assistant, University of Virginia, 2025
Assisted Dr. Lindsay Ivey-Burden in supporting senior design teams with project scoping, technical analysis, and professional report development.
Graduate Teaching Assistant, University of Virginia, 2026
Assisted Dr. Gomez with instruction support and quantitative problem-solving, including mechanics fundamentals (stress–strain, axial/torsional response, beam behavior). Supported sessions and grading for assignments.
Graduate Teaching Assistant, University of Virginia, 2026
Assisted Dr. Negin Alemazkoor with course content and student project support focused on climate risk, infrastructure resilience, and uncertainty-aware planning approaches.