About me

Welcome! I’m a polar climate scientist interested in how natural and anthropogenic processes lead to changes in the Earth’s sea ice cover, and how well we can predict these changes on timescales of days to decades. To do this, I use machine learning methods in conjunction with large-scale remote sensing observations to explore different aspects of sea ice predictability, and also to understand how we can improve the representation of sea ice processes in climate models. Sea ice plays a crucial role in both environmental and social systems, as a regulator on Earth’s temperature, and also an integral platform for connecting Arctic communities. Understanding how sea ice has changed, and will continue to change, due to global warming is therefore crucial for assessing the impacts of sea ice loss on climate and society.

I am currently a postdoctoral research associate at Princeton University, working within the Geophysical Fluid Dynamics Laboratory Ocean and Cryosphere division. Here, I am hired under the M2LInES initiative (Multi-scale Machine Learning In coupled Earth System modeling), which is a programme dedicated to the improvement of climate model physics through novel machine learning techniques. Prior to this, I obtained my PhD in polar climate and machine learning from University College London. For more information on my research activities, you can check out my Google Scholar.

I am also developing a series of educational Python notebooks which cover the fundamental concepts of various statistical and ML methodologies. The aim is to cover concepts related to linear algebra, as well as supervised/unsupervised learning techniques; starting from linear regression and working towards deep neural networks. These notebooks will be routinely uploaded to the Code page of this website.