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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

Long-Term Power Grid Planning Under Climate Change and Energy Transition

Analytical review synthesizing climate change impacts on power systems, integrating demand growth, infrastructure degradation, extreme events, uncertainty modeling, and renewable transition planning.

Leveraging Hyperscale Data Centers as Restoration Anchors for Grid Resilience

Graph Neural Network–based reinforcement learning framework for integrating hyperscale data centers into post-disaster distribution system restoration.

Sector-Coupled Capacity Expansion Modeling: Data Centers and Heat Pump Electrification

Capacity expansion modeling framework quantifying interactions between hyperscale data center growth, residential electrification, and long-term energy affordability.

Probabilistic Energy Transition Modeling Framework

Probabilistic energy transition planning framework integrating capacity expansion modeling, hierarchical Monte Carlo simulation, and machine learning surrogates.

GCAM–CDR Coupled Uncertainty Framework

Probabilistic integrated assessment modeling framework coupling carbon dioxide removal deployment with power-sector evolution under deep uncertainty.

Precision-Guaranteed GNN-Based Predictive Compression for Large-Scale Infrastructure Data

Graph neural network framework for prediction-based compression with explicit decompression error guarantees across large-scale spatio-temporal datasets.

Applications of Graph Neural Networks in Civil Infrastructures

Systematic review of Graph Neural Network methodologies across transportation, power, water, and structural infrastructure systems, identifying research gaps and future directions.

Hybrid Chance-Constrained Optimal Power Flow Using Enhanced Multi-Fidelity Graph Neural Networks

Surrogate-accelerated chance-constrained optimal power flow framework combining multi-fidelity graph neural networks with hybrid constraint validation under renewable and load uncertainty.

MAGNN-A2C: Graph Neural Network–Based Multi-Agent Reinforcement Learning for Post-Hurricane Grid Restoration

Topology-aware multi-agent reinforcement learning framework for scalable power distribution system restoration after hurricanes.

Statewide Prioritization of Stormwater Infrastructure for Climate-Resilient Inspection and Maintenance

Geospatial risk-based decision-support system for statewide prioritization of stormwater infrastructure inspection and maintenance under climate stressors.

Multi-Fidelity Graph Neural Networks for Efficient Power Flow Analysis

Residual multi-fidelity graph neural network framework enabling scalable probabilistic power flow analysis under high-dimensional renewable and load uncertainty.

Machine Learning–Based Performance Prediction for High-RAP Asphalt Mixtures

Regression-based machine learning framework for predicting rutting, cracking, and moisture resistance of asphalt mixtures and identifying feasible design regions under traffic-level constraints.

Temporal Graph Neural Networks for Failure and Cause Prediction in Transmission Networks

Utility-scale graph learning framework for structure-level failure localization and cause prediction across large transmission networks.

publications

Performance Evaluation of Asphalt Mixtures Containing Different Proportions of Alternative Materials

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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Long-Term Power Grid Planning: Navigating Climate Change and Energy Transition Challenges

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.

Hybrid Chance-Constrained Optimal Power Flow Under Load and Renewable Generation Uncertainty Using Enhanced Multi-Fidelity Graph Neural Networks

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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Multi-Fidelity Graph Neural Networks for Efficient Power Flow Analysis Under High-Dimensional Demand and Renewable Generation Uncertainty

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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Graph Neural Networks for Precision-Guaranteed Compression of Large-Scale Data

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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Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems

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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Interactions Between Data Center Load Growth, Residential Heat Pump Adoption, and Energy Affordability

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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GNN-based Multi-Agent Reinforcement Learning for Power Distribution Recovery

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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Identifying Key Uncertainties in Energy Transitions: A Puerto Rico Case Study

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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