Sigrid Passano Hellan

Hi! I'm Sigrid, a machine learning researcher working on climate applications. I'm a senior researcher at NORCE Research and the Bjerknes Centre for Climate Research, and have a PhD in Data Science from the School of Informatics, University of Edinburgh.

My research interests are in how we can reliably and effectively leverage machine learning for climate applications. To do so, I leverage probabilistic modelling, transfer learning and hyperparameter optimisation. Currently, I have several projects on downscaling climate data. During my PhD I worked on Bayesian optimisation methodology for hyperparameter optimisation and air pollution monitoring. You can read more about my project here.

September 2026: Our paper Lightweight Probabilistic Downscaling from a Deterministic Base Model got accepted to the Workshop on Advances in Representation Learning for Earth Observation at NeurIPS.
August 2026: We are recruiting a PhD position as part of DYNAMIC-AI. See the listing here.
June 2026: I will be leading the Bjerknes strategic project DYNAMIC-AI, on the Dynamics of Unprecedented and Compound Extremes using AI.
June 2026: We finished teaching the Bjerknes Training Programme in Machine Learining.
November 2025: We hosted the Norwegian Workshop on Machine Learning for Weather and Climate as part of the project AIGLE. With Olav Ersland at the Norwegian Meteorological Institute.
September 2025: I gave a presentation on Hyperparameter tuning, Bayesian optimisation and applications related to climate change to the Learning Machines seminar. You can watch the recording on YouTube.
May 2025: Our paper Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation got accepted to AutoML. Looking forward to the conference!
April 2025: Our paper Bayesian Optimisation Against Climate Change: Applications and Benchmarks got accepted to Lion19. Looking forward to the conference!
February 2025: I've joined the core team of Climate AI Nordics.
December 2024: I spoke to Tori Pedersen at the Bjerknes centre about my new job and about ML for weather prediction. Read what we talked about here.
October 2024: I attended the CATER summer school, where I gave a presentation on AI and weather prediction.
January 2024: I passed my viva (defended my PhD). Thank you to Joaquin Vanschoren and Arno Onken for an insightful and friendly viva!
October 2023: I'm very grateful to have won a grant from G-Research towards attending NeurIPS 2023 and presenting our workshop paper Data-driven Prior Learning for Bayesian Optimisation.
April 2023: I've recently completed an internship with AWS in Berlin, working on hyperparameter optimisation of ML models. I also contributed to GluonTS and Syne Tune.
December 2022: Tiffany Vlaar and I organised the Early-Career Researchers in Climate and ML Social at NeurIPS 2022.