Instructors

Jordi Bolibar

Jordi is a Postdoctoral researcher at IGE, Université Grenoble Alpes (France). Jordi’s research focuses on applying machine learning and physical models to model, understand and predict climate-glacier interactions at large geographical scales. He's currently working on ODINN, a project and modelling framework to investigate new ways to learn laws governing climate-glacier interactions using machine learning and differential equations.

Regine Hock

Regine is a Professor at the University of Oslo. She is a hydrologist and glaciologist using modeling and field data to assess glacier mass changes and to predict the response of glaciers to future climate change and assess the implications on sea-level and hydrology.

Konstantin Maslov

Konstantin is a PhD student at the Faculty of Geo-Information Science and Earth Observation, University of Twente. He received his bachelor's and master's degrees in Computer Science from Tomsk Polytechnic University, Russia. His main research interests are methods of machine learning applied to Earth Observation data. Most of the time he deals with convolutional neural networks and vision transformers for semantic image segmentation but he is also keen on other approaches for image analysis and problems at the intersection of Remote Sensing and Data Science. He is currently working on global glacier mapping and glacier mass balance estimation using deep learning .

Facundo Sapienza

Facundo (Facu) Sapienza is a PhD candidate at the Department of Statistics at the University of California, Berkeley. Facu’s work includes the use of neural differential equations in the modeling of ice flow, where sub-parts of the governing differential equation can be parametrized by physical-informed proxies; feature detection algorithms in ice sheets using remote sensing data; and machine learning methods applied to paleomagnetism and planetary sciences. He is also interested in the development and communication of open-source tools for scientific computing.

Thomas Schellenberger

Thomas is a researcher at the University of Oslo. He investigates the dynamics of glaciers using remote sensing data. He uses SAR and optical satellite data to extract glacier velocities to monitor glacier surges and to estimate frontal ablation of marine-terminating glaciers. Thomas current focus is on developing machine learning algorithms to mapping glacier extent and estimating glacier mass balance from satellite data.

Kamilla Hauknes Sjursen

Kamilla is a PhD candidate at the Western Norway University of Applied Sciences. She investigates the use of Bayesian and Machine Learning approaches to model and understand past and future changes in mass balance of Norwegian glaciers.