Reza Arghandeh
Field of work
Professor Reza Arghandeh currently serves as the leader of the Data Science and Artificial Intelligence Group (HVL DS-AI) and as the director of the Connectivity, Information & Intelligence Lab (Ci2Lab) at the Western Norway University of Applied Sciences (HVL) in Bergen, Norway.
In addition to his role at HVL, Prof. Arghandeh holds the title of Research Professor in the Electrical and Computer Department at Florida State University, USA. He previously served as an assistant professor at the same institution from 2015 to 2018. Prior to his tenure at Florida State University, he conducted groundbreaking research as a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences at UC Berkeley from 2013 to 2015, USA.
He completed his Ph.D. in Electrical Engineering with a specialization in Power Systems at Virginia Tech, USA (2013). He holds Master’s degrees in Industrial and System Engineering from Virginia Tech (2013) and Energy Systems from the University of Manchester and KNTU (2008).
His research interests include applied Artificial Intelligence for spatiotemporal and geospatial data analysis related to complex networks. Core applications include climate adaptation solutions for energy and infrastructure systems.
His research has been supported so far by the U.S. National Science Foundation, the U.S. Department of Energy, the European Space Agency, the European Commission, and the Research Council of Norway.
Kunstig intelligens
Maskinlæring
- Artificial Intelligence & Machine Learning
- Causal Inference
- Computer Vison and Image Processing
- AI for Remote Sensing (Optical and SAR Satellite Images)
- AI for Infrastructure Networks Monitoring
- AI for Power System Monitoring and Operation
- AI for Infrastructure Resilience Against Climate Change
- Connectivity, Information & Intelligence Lab (Ci2Lab)
- HVL Data Science and Artificial intelligence Group (HVL DS-AI)
- Publications: Google Scholar
Publications
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Exploring the application of machine-learning techniques in the next generation of long-term hydropower-thermal scheduling
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Traffic monitoring system design considering multi-hazard disaster risks
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High-resolution mapping of forest structure from integrated SAR and optical images using an enhanced U-net method
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MARU-Net: Multiscale Attention Gated Residual U-Net With Contrastive Loss for SAR-Optical Image Matching
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Advancements in super-resolution methods for smart meter data
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Sensor Data Fusion for Monitoring Unstable Rock Slopes
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Multimodal Asynchronous Kalman Filter for monitoring unstable rock slopes
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Sensor Fusion for Monitoring Unstable Rock Slopes-A Case Study from the Stampa Instability, Norway
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A Survey on SAR and Optical Satellite Image Registration
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Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition
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Tree Species Classification Using High-Resolution Satellite Imagery and Weakly Supervised Learning
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Remote sensing-based comparative damage assessment of historical storms and hurricanes in Northwestern Florida
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Roadway Vulnerability Assessment against Hurricanes Using Satellite Images
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Self-organizing maps for scenario reduction in long-term hydropower scheduling
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Day-ahead inflow forecasting using causal empirical decomposition
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Synchrophasor Applications to Support Power Distribution Networks
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Data Driven Reliable and Resilient Energy System Against Disasters
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Inflow Forecasting Based On Principal Component Analysis and Long Short Term Memory
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Inflow Forecasting Based on Principal Component Analysis and Long Short Term Memory
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Automated 3D Vegetation Detection Along Power Lines using Monocular Satellite Imagery and Deep Learning
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Post-Hurricanes Roadway Closure Detection using Satellite Imagery and Semi-Supervised Ensemble Learning
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Automated Satellite-based Assessment of Hurricane Impacts on Roadways
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Automated Power Lines Vegetation Monitoring using High-Resolution Satellite Imagery
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City transportation network vulnerability to disasters: the case of Hurricane Hermine in Florida
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Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning
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Exploring Correlations Between Vehicle Travels and Tropospheric Nitrogen Dioxide (NO2) Density Among Florida Counties Impacted by COVID-19
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Leveraging Remote Sensing Indices for Hurricane-induced Vegetative Debris Assessment: A GIS-based Case Study for Hurricane Michael
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Post-Hurricane Vegetative Debris Assessment Using Spectral Indices Derived from Satellite Imagery
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Resilience Characterization for Multilayer Infrastructure Networks
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Distribution line parameter estimation driven by probabilistic data fusion of D-PMU and AMI
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Synchronized Measurements and Their Applications in Distribution Systems: An Update
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Developing City-wide Hurricane Risk Maps using Real-life Data on Infrastructure, Vegetation and Weather: A GIS-based Case Study in Northwest Florida
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Developing City-Wide Hurricane Impact Maps using Real-Life Data on Infrastructure, Vegetation and Weather
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Understanding citizens' communication channel preferences during natural disasters: A synchronicity-based, mixed-methods exploration using survey and geospatial analysis
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Power quality event classification using optimized Bayesian convolutional neural networks
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Statistical and spatial analysis of Hurricane-induced roadway closures and power outages
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Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System
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The Road toward Smart Cities: A Study of Citizens’ Acceptance of Mobile Applications for City Services
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Analyzing COVID-19 Impacts on Vehicle Travels and Daily Nitrogen Dioxide (NO2) Levels among Florida Counties
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Guest Editorial Theory and Application of PMUs in Power Distribution Systems
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Building Characterization through Smart Meter Data Analytics: Determination of the Most Influential Temporal and Importance-in-prediction based Features
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Machine learning based disaggregation of air-conditioning loads using smart meter data
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Cause identification of electromagnetic transient events using spatiotemporal feature learning
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Design of Modular Multilevel Converters for the Shipnet in medium Voltage DC All-Electric Ships
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Topology Detection in Power Distribution System using Kernel-node-map Deep Networks
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Combined Electricity and Traffic Short-Term Load Forecasting Using Bundled Causality Engine
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Shape Preserving Incremental Learning for Power Systems Fault Detection
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Clustering Household Electrical Load Profiles Using Elastic Shape Analysis
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Data Mining Techniques and Tools for Synchrophasor Data
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Multi-Task Bayesian Spatiotemporal Gaussian Processes for Short-term Load Forecasting
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Bayesian Spatiotemporal Gaussian Process for Short-term Load Forecasting Using Combined Transportation and Electricity Data
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Causality-based Combined Power and Transportation Resilience (Co-Resilience) during Hurricanes
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Multi-Network Vulnerability Causal Model for Infrastructure Co-Resilience
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UrbanBox: a Low Cost End-to-End Platform for Smart City Sensing
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Multivariate Deep Causal Network for Time Series Forecasting in Interdependent Networks
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Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks
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Power Resilience Assessment from Physical and Socio-Demographic Perspectives
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Multiple Kernel Semi-representation Learning with Its Application to Device-Free Human Activity Recognition
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Big Data Application in Power Systems
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Power Distribution Network Topology Detection With Time-Series Signature Verification Method
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Data-Driven Event Detection in Distribution Power Systems
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Shape Preserving Incremental Learning for Power Systems Fault Detection
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Design Automation for Smart Building Systems
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Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions
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Co-resilience Assessment of Hurricane-induced Power Grid and Roadway Network Disruptions: A Case Study in Florida with a Focus on Critical Facilities
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Assessment of Emergency Facility Accessibility in the Presence of Hurricane-Related Roadway Closures and Prediction of Future Roadway Disruptions
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Causal Markov Elman Network for Load Forecasting in Multinetwork Systems
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Multivariate Deep Causal Network for Time series Forecasting in Interdependent Networks
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Data-Driven and Hurricane-Focused Metrics for Combined Transportation and Power Networks Resilience
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Real-Time Simulation and Hardware-in-the-Loop Testbed for Distribution Synchrophasor Applications
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Online power quality events detection using weighted Extreme Learning Machine
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Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective
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Moving Toward Agile Machine Learning for Data Analytics in Power Systems
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Non-parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis
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Nonparametric Event Detection in Multiple Time Series for Power Distribution Networks