I am interested in developing simple and interpretable statistical machine learning methodologies to address challenges that arise in climate science. My recent work has focused on climate model emulation using physically-informed models and statistical downscaling. The tools I use mostly draw from the theory of reproducing kernel Hilbert spaces and Gaussian processes, for which I enjoy a fond theoretical interest.
I am a final year PhD student in the Oxford Computational Statistics and Machine Learning group at the University of Oxford, supervised by Dino Sejdinovic, Tom Rainforth and Athanasios Nenes as part of the iMiracli innovative training network of aerosols-cloud interactions and machine learning.
I am a co-creator of Nechfate [neʃfɛt], an online media that popularizes climate change, its impacts, and adaptation solutions in Morocco. For non-Arabic speakers, Nechfate refers initially to an aridification but also means a deterioration of economic conditions.
Through short, illustrated, and data-driven articles, our goal is to inform readers about Morocco’s challenges in terms of Climate Change, Water & Agriculture, and Governance & Societ️y. Come check us out on Instagram and Linkedin.