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Marc Girona-Mata

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I am a PhD student at the Computational and Biological Learning Lab (University of Cambridge) and the British Antarctic Survey. My research interests include probabilistic modelling for environmental and climate sciences. I am currently working on improving precipitation and river flow predictions in data-scarce mountain regions, such as the Himalayas, and exploring how this translates into actionable information for better decision making.

I hold an MRes in Environmental Data Science from the University of Cambridge, an MSc in Hydrology and Water Resources Management from Imperial College London, and an MEng in Civil Engineering from the Polytechnic University of Catalonia (BarcelonaTECH).

Marc Girona-Mata's Projects

bad-boids icon bad-boids

A deliberately badly programmed implementation of Boids for teaching

baspy icon baspy

Making it far easier to read in and work with large volumes of climate model output from CMIP5/6

bcdp icon bcdp

Bias correction of mountain daily precipitation simulations from regional climate models using in-situ observations

clim-data icon clim-data

Tools for downloading, parsing and processing climate data from various sources

convcnp icon convcnp

Implementation of the Convolutional Conditional Neural Process

convcnps_hydro icon convcnps_hydro

Using Convolutional Conditional Neural Processes (ConvCNPs) for rainfall runoff modelling and hydrological prediction tasks

gp_algorithm icon gp_algorithm

Implementation of the Grassberger-Procaccia algorithm to estimate the Correlation Dimension of a set of points

hydro-nps icon hydro-nps

Neural processes for hydrological modelling

lake-snowfall-sensors icon lake-snowfall-sensors

Lake as snowfall sensors: automating data post-processing to recover snowfall from lake pressure signal

lstm_for_pub icon lstm_for_pub

Code for our WRR paper "Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning"

maml-pytorch icon maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

mgironamata icon mgironamata

A beautiful, simple, clean, and responsive Jekyll theme for academics

neuralhydrology icon neuralhydrology

Python library to train neural networks with a strong focus on hydrological applications.

pddp-mountains icon pddp-mountains

probabilistic downscaling of daily precipitation for ungauged mountain locations

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

wntr icon wntr

An EPANET compatible python package to simulate and analyze water distribution networks under disaster scenarios.

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