A few facts about me (in no particular order):
- Born in Santa Marta, Colombia
- Researcher in AI, data scientist
- Professional note-taker and grower of a knowledge garden
- Pythoneer and scientific software developer
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World-traveller
- Ex-astronomer/astrophysicist
- Curious about entrepreneurship
- Proud dad
- Metalhead and avantgarde music lover
- Polyglot (es, en, fr, ru)
I received a PhD from the University of Liege, Belgium, where I worked on Computer Vision for the task of extrasolar planets detection, pioneering the application of Deep Learning to the field of astronomical high-contrast imaging.
After my PhD, I joined the Grenoble Alpes Data Institute (France) as a junior research chair in Data Science for Earth, Space and Environmental sciences. My work at the Grenoble Alpes Data Institute consisted in interfacing scientific data analysis and Machine Learning, drawing inspiration from the AI literature to create innovative algorithms for multi-dimensional data processing (e.g. image sequences with temporal, spectral and other additional dimensions). My interest in open research led me to spend my time developing open-source scientific computing tools, organizing or participating in data challenges and spreading the word about open research practices.
The enriching experience of these two years at the Grenoble Alpes Data Institute allowed me to diversify my research interests and to expand my comfort zone when it comes to carrying out inter- and multi-disciplinary projects. Moreover, I had the fortune to participate in a few European entrepreneurship events that woke up the entrepreneur side of me. After co-funding a couple of start ups and feeling the temptation to leave academia, I decided instead to continue doing research and fully focus my skills on a field related to the Earth and the environment.
This is why nowadays, I work as a STARS (MSCA-COFUND) postdoctoral fellow in AI for Earth Sciences, within the Computational Earth Sciences group at the Barcelona Supercomputing Center. As a researcher in AI applied to Earth Sciences, I am interested in the development of machine and deep learning algorithms for topics, such as statistical downscaling and bias correction techniques, data-driven parameterisations, and the study of extreme climate events. I care a lot about open source and scientific software development.
Little did I know that switching disciplines mid-career is usually a very bad idea. The main challenges being: building credibility and a network of collaborators in a new field, and keeping a steady production of publications (unfortunately, the main currency in the modern academic world). In spite of this, I feel fortunate with what I do: contributing to exciting inter-disciplinary projects focused on the most important planet we know of, Earth! 