My main interest lies in statistical learning—especially in how causality and probability shape learning theory and help us uncover stable mechanisms for making robust predictions. I explore these ideas in the context of climate science, with a particular focus on understanding and attributing the drivers of climate change.
I amm a PhD student at the Universitat de València (Spain), supervised by Gustau Camps-Valls and Gherardo Varando. My research focuses on developing and understanding Detection and Attribution (D&A) of Climate Change methods in a causal inference framework.
More broadly, I’m interested in learning theory and how to combine different sources of data and knowledge to make predictions and understand complex phenomena. My academic interests span causal inference, Bayesian inference (especially as inductive logic), kernel methods and climate change D&A.
I am also preparing a course on probabilistic climate modelling. It will explore probability as inductive logic, uncertainty quantification, Markov Chain simulations in climate models, calibration and basic concepts in detection and attribution.
Thesis: Causal Represntation Learning for Detection and Attribution of Climate Change.
Project: Learning causal representation and develop causal tools robust to distributional shifts for spatio-temporal and potentially high-dimensional data. Apply these methodologies to Detection and Attribution of climate change.
Advisors: Gustau Camps-Valls, Gherardo Varando
📄 Paper accepted at AISTATS 2025
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