Faculty

Institution

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Negin Alemazkoor

Assistant Professor of Engineering Systems and Environment

University of Virginia

Affiliations

School of Engineering and Applied Science

Department of Civil and Environmental Engineering

Negin Alemazkoor is an Assistant Professor of Engineering Systems and Environment at the University of Virginia. She earned her B.S. in Civil Engineering from Sharif University of Technology, an M.S. in Transportation Engineering from Texas A&M University, and a Ph.D. in Civil Engineering with a minor in Statistics from the University of Illinois at Urbana-Champaign. Her research integrates machine learning, graph neural networks, and physics-informed modeling to address complex engineering systems, focusing on spatiotemporal dynamics on unstructured meshes, flood forecasting and hazard mapping, power distribution system recovery after hurricanes, and solar power generation forecasting. Recent publications include works on graph-physics aware recurrent convolutional networks and interpretable physics-informed graph neural networks. She has received major awards, including a $1M NSF grant for AI workforce development and an AI for Youth award, and collaborates on interdisciplinary projects such as energy transition uncertainty analysis in Puerto Rico.

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Negin Alemazkoor

University of Virginia

Assistant Professor of Engineering Systems and Environment

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computer science data science machine learning neural network (machine learning) graph neural networks electrical engineering applied mathematics scientific computing computational intelligence smart grid intelligent transportation systems flood risk management hydroinformatics control optimization chaos theory power system analysis forecasting partial differential equations natural disasters numerical analysis computational mathematics disaster risk science machine learning research scientific data hydrometeorology numerical simulations numerical algorithms energy research nonlinear dynamics nonlinear science nonlinear analysis flood control sampling (signal processing) distributed energy generation energy prediction power system optimization traffic monitoring traffic prediction hydrological modeling load forecasting polynomial chaos expansions railway traffic prediction modelling weather forecasting climate projections disaster research climate forecasting climate change mitigation computer-aided engineering principal component analysis hydrological disaster urban hydrology compressive sensing reliability prediction reliability modelling failure prediction deterioration modeling energy forecasting energy generation sampling theory data engineering generative ai delay category train delay primary level future hurricane frequency neural networks flood forecasting deep learning-based downscaling smart-meter big data compressive sampling approach incremental sparsity multiple scales track geometry high-dimensional demand heterogeneous data

Notable Scholarly Works

Notable Federal Grants

Collaborators

A Graph Neural Network-Based Multi-Agent Reinforcement Learning Model for Efficient Power Distribution System Recovery After Hurricanes. Reliability Engineering & System Safety (2026)

control optimizationagent-based systemmulti-agent planningsystems engineeringreinforcement learningsmart gridpower system restorationintelligent energy systemmultiagent systemsmulti-agent learningevolutionary intelligenceenergy management

G-Parc: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes. arXiv (Cornell University) (2026)

deep learningcomputer sciencespatiotemporal dynamicsunstructured meshesgraph neural networksscientific computingdata sciencephysic aware machine learningcomputer visionrecurrent neural networksneural network (machine learning)

A Graph Attention Network Framework for Generalized-Horizon Multi-Plant Solar Power Generation Forecasting Using Heterogeneous Data. Renewable Energy (2025)

Md Abul HasnatSomayeh AsadiNegin Alemazkoor

heterogeneous datacomputer sciencecontrol optimizationsolar powermachine learningenergy researchrenewable energyenergy generationenergy predictionknowledge graphsgraph neural networkssystems engineeringelectricity generationmachine learning researchdistributed energy generationenergy forecastingelectrical engineeringsmart grid

Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems. SSRN Electronic Journal (2025)

structural systemsintelligent systemsnetwork analysiscomputational intelligenceinfrastructure engineeringgraph neural networkssystems engineeringgraph signal processingcivil engineeringcivil infrastructuresneural network (machine learning)structural health monitoring

Deep Learning-Based Downscaling of Global Digital Elevation Models for Enhanced Urban Flood Modeling. Journal of Hydrology (2025)

Zanko ZandsalimiSergio A. BarbosaNegin AlemazkoorJonathan L. GoodallMajid Shafiee-Jood

deep learningurban planningdisastersdeep learning-based downscalingflood risk managementnatural disastersenvironmental sciencehydrologyearth sciencedisaster risk scienceflood controlenvironmental engineeringprincipal component analysishydrometeorologygeoscientific model developmenthydroinformaticsurban hydrologyhydrological modelinghydrological disaster

End-To-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems. (2025)

Zanko ZandsalimiMehdi TaghizadehSavannah Lee LynnJonathan L. GoodallMajid Shafiee-JoodNegin Alemazkoor

stormwatercomputer sciencewater resourceshydroinformaticscomputational intelligencewater resource managementmachine learningsurrogate modelsinfrastructure engineeringgraph neural networkshydrological modelingdata sciencehydrologystormwater systemsflood risk managementcivil engineeringmachine learning researchneural network (machine learning)

Identifying Key Uncertainties in Energy Transitions With a Puerto Rico Case Study. Nature Communications (2025)

energy systemsenergy institutestatisticsenergy strategiesenergy planninguncertainty analysisenergy researchenergy transitionssystems engineeringgeographykey uncertaintiesenergy transitionenergy structure transitionenergy assessmentenergy issues

Integrated Sizing and Management of Residential Energy Systems for Electric and Hydrogen Vehicle Charging. Journal of Building Engineering (2025)

energy systemsenergy conversionpower deliveryresidential energy systemsenergy infrastructurehydrogen vehiclesystems engineeringenergy efficiencyincluding vehicle-to-gridelectricity generationelectric vehicleshydrogen-based microgridssmart gridsustainable energy technologieselectric power systemselectrical engineeringenergy management

Interpretable Physics-Informed Graph Neural Networks for Flood Forecasting. Computer-Aided Civil and Infrastructure Engineering (2025)

computational intelligencenatural disastershydroinformaticsgraph neural networksdata scienceflood forecastingforecastingflood risk managementdisaster risk scienceneural network (machine learning)

Multi-Fidelity Graph Neural Networks for Efficient and Accurate Flood Hazard Mapping. Environmental Modelling & Software (2025)

deep learningflood controlhydroinformaticsnatural disastersmachine learninghydrologic hazardgraph neural networksdata sciencehydrologynatural hazardsflood risk managementcivil engineeringneural network (machine learning)

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Negin Alemazkoor

Assistant Professor of Engineering Systems and Environment

University of Virginia

Affiliations

School of Engineering and Applied Science

Department of Civil and Environmental Engineering

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Negin Alemazkoor

University of Virginia

Assistant Professor of Engineering Systems and Environment

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

A Graph Neural Network-Based Multi-Agent Reinforcement Learning Model for Efficient Power Distribution System Recovery After Hurricanes. Reliability Engineering & System Safety (2026)

G-Parc: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes. arXiv (Cornell University) (2026)

A Graph Attention Network Framework for Generalized-Horizon Multi-Plant Solar Power Generation Forecasting Using Heterogeneous Data. Renewable Energy (2025)

Md Abul HasnatSomayeh AsadiNegin Alemazkoor

Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems. SSRN Electronic Journal (2025)

Deep Learning-Based Downscaling of Global Digital Elevation Models for Enhanced Urban Flood Modeling. Journal of Hydrology (2025)

Zanko ZandsalimiSergio A. BarbosaNegin AlemazkoorJonathan L. GoodallMajid Shafiee-Jood

End-To-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems. (2025)

Zanko ZandsalimiMehdi TaghizadehSavannah Lee LynnJonathan L. GoodallMajid Shafiee-JoodNegin Alemazkoor

Identifying Key Uncertainties in Energy Transitions With a Puerto Rico Case Study. Nature Communications (2025)

Integrated Sizing and Management of Residential Energy Systems for Electric and Hydrogen Vehicle Charging. Journal of Building Engineering (2025)

Interpretable Physics-Informed Graph Neural Networks for Flood Forecasting. Computer-Aided Civil and Infrastructure Engineering (2025)

computational intelligencenatural disastershydroinformaticsgraph neural networksdata scienceflood forecastingforecastingflood risk managementdisaster risk scienceneural network (machine learning)

Multi-Fidelity Graph Neural Networks for Efficient and Accurate Flood Hazard Mapping. Environmental Modelling & Software (2025)

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10 / page