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.
Courses taught
Kunstig intelligens
Maskinlæring
Research areas
- 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
Research groups
- Connectivity, Information & Intelligence Lab (Ci2Lab)
- HVL Data Science and Artificial intelligence Group (HVL DS-AI)
- Publications: Google Scholar
- DAT300, Master's thesis, Fall 2024
- PCS900, PhD Dissertation in Computer Science: Software Engineering, Sensor Networks and Engineering Computing, Spring 2025
- PCS900, PhD Dissertation in Computer Science: Software Engineering, Sensor Networks and Engineering Computing, Fall 2024
- PCS956, Research Trends in Applied Machine Learning, Fall 2024
Publications
-
Big data application in power systems
-
Transformer-based land use and land cover classification with explainability using satellite imagery
-
Diagnostics of Exercise-Induced Laryngeal Obstruction Using Machine Learning: A Narrative Review
-
Electricity demand forecasting at distribution and household levels using explainable causal graph neural network
-
Exploring the application of machine-learning techniques in the next generation of long-term hydropower-thermal scheduling