Negin Alemazkoor

About

Negin Alemazkoor is an Assistant Professor at the University of Virginia School of Engineering and Applied Science. Her research focuses on developing sensing and computing methodologies for the fast and reliable analysis of smart and interconnected infrastructure systems under uncertainty. Her work is inherently multi-disciplinary and aims to advance the reliability and resilience of infrastructure systems by facilitating fast and accurate system analysis, which leads to optimal system operation and management.

Research topics

• Computer Science
• Machine Learning
• Data Mining
• Economics
• Real-time computing
• Medicine
• Database
• Risk analysis (engineering)
• Environmental resource management
• Internal medicine

Selected publications

• Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

  Scientific Reports · 2020 · 47 citations

  1st authorCorresponding

  Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here,…

  PublisherOA PDFDOI

• Interpretable physics-informed graph neural networks for flood forecasting

  Computer-Aided Civil and Infrastructure Engineering · 2025-04-15 · 29 citations

  articleSenior author
  PublisherDOI

• Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling

  Computer-Aided Civil and Infrastructure Engineering · 2024-07-26 · 29 citations

  articleOpen accessSenior authorCorresponding

  Accurately predicting the dynamics of complex systems governed by partial differential equations (PDEs) is crucial in various applications. Traditional numerical methods such as finite element methods (FEMs) offer precision but are resource-intensive, particularly at high mesh resolutions. Machine learning–based surrogate models, including graph neural networks (GNNs), present viable alternatives by reducing computation times. However, their accuracy is significantly contingent on the availabili…

  PublisherOA PDFDOI

• Deep learning-based downscaling of global digital elevation models for enhanced urban flood modeling

  Journal of Hydrology · 2025-01-16 · 25 citations

  article
  PublisherDOI

• Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering

  IEEE Access · 2022 · 25 citations

  1st authorCorresponding

  Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to c…

  DOI

Frequent coauthors

• Hadi Meidani

  University of Illinois Urbana-Champaign

  12 shared

• Mazdak Tootkaboni

  10 shared

• Arghavan Louhghalam

  8 shared

• Mehdi Taghizadeh

  University of Virginia

  8 shared

• Mark Burris

  6 shared

• Harsh Anand

  6 shared

• Roshanak Nateghi

  Purdue University West Lafayette

  6 shared

• Md Abul Hasnat

  University of Virginia

  4 shared

Labs

• Alemzkoor Research GroupPI

Education

• PhD, Civil and Environmental Engineering

  University of Illinois at Urbana-Champaign

  2019

Similar researchers at University of Virginia

• James A. Smithh-77
• Larry Bandh-72
• James H. Lamberth-47