Hello! 👋

I'm a PhD research fellow in Deep Learning applied to offshore wind technologies at the University of Oslo. My work is in collaboration with Institute for Energy Technology (IFE) on a project entitled Electrification of Oil & Gas Installation by Offshore Wind (ELOGOW). I have an M.Eng. degree from Durham University in New and Renewable Engineering and have a keen interest in ML applied to various fields that positively impact society. As part of my PhD, most of my research have been concerned with wind park modelling and time-series forecasting using Graph Neural Networks, attention mechanisms, bayesian deep learning and recent advancements in a range of Transformer-based architectures. Upon graduating in October 2023, I'm starting a new position as a Data Science Consultant at BearingPoint within Data Science and AI. My full CV can be found here.

  • 2023
    BearingPoint: Consultant - Data Science and AI
  • 2023 (Expected)
    Ph.D. University of Oslo
  • 2020
    M.Eng. Durham University
  • 2019
    Vodafone Internship - Technology
  • 2016
    Upper Secondory Education
    Oslo Commerce School

Some key skills

  • Python, MATLAB, R, CSS/HTML, C#/C++ (Basic)
  • Pytorch, TensorFlow, Jupyter, etc.
  • LaTeX, Git, Docker, Azure
  • Skiing, Water sports, Cooking, Guitar

Selected Publications

Full list of publications can be found here

Relative Evaluation of Probabilistic Methods for Spatio-Temporal Wind Forecasting

Bentsen, LØ, Warakagoda, ND, Stenbro, R, Engelstad P, Journal of Cleaner Production, 2024

[Paper]

Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures

Bentsen, LØ, Warakagoda, ND, Stenbro, R, Engelstad P, Applied Energy, 2023

[Paper]

Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks.

Bentsen, LØ, Warakagoda, ND, Stenbro, R, Engelstad P, Journal of Physics: Conference Series, 2022

Best Scientific Content Award: DeepWind22 [Paper]