Who am I?

I’m a Swiss-Brazilian Biologist born and raised in a (very!) tiny village in Switzerland, and who happens to have moved to a beautiful coastal town in Brazil (Natal) during my teens. Ever since I was little, I was immensely fascinated by the sea and all its biodiversity. Living literally 10 min from the salty waters, it was thus not difficult to decide what I wanted to become: a biologist.

And so I became a biologist…but not a classical one such as David Attenborough. Rather, I became more of an ecological detective where I spent most of my academic carrer behind a computer screen analyzing ecological data. And I simply love that!

Poor genuine student who thought that biology is all about identifying/interacting with animals and plants. Nearly all biological and ecological laws are ultimately guided by mathematical and statistical principles, and more than ever, data science has been playing a vital role in a biologist’s daily routine.

Nowadays, I found myself bridging the gap between biology/ecology and statistics, while at the same time creating deeper roots towards data science.

Apart from that “geeky” part, I like to spend my time with photography, reading books, watching movies/series (Breaking Bad is my all-time favorite!), traveling, hiking…and studying (yep!). I also like to think about life and everything that comes with it, and share/debate these philosophical thoughts with friends.

Academic journey

During my undergraduate, I devoted most of my time to subjects related to marine biology where I eventually found my passion (out of many others!) in the lab of fisheries biology. There, I stepped effectively for the first time into the world of data science and statistics, where my main role was to organize and analyze datasets from the local artisanal fisheries. A new passion was quickly born, namely statistics!

To deepen my statistical skills to address contemporary fisheries issues, I moved yet to another tiny town called Rio Grande in the south of Brazil, where I pursued my MSc in the lab of environmental statistics. Working amidst an interdisciplinary environment with statistical groundings, and where statistical debates were brought on the table every week or so was definitely a game changer for me. Allied to fisheries, I found myself immersed in a fertile ground where I could rapidly expand both my statistical and programming skills, besides developing myself as a quantitative fisheries scientist. I learned (and fell in love with) Bayesian and spatial statistics, where I ultimately gathered those two subjects into my thesis (see resulting paper here).

Later on, I moved back to Europe, more specifically to Denmark ( Copenhagen), to conduct a PhD at the section for ecosystem based marine management. I was again very fortunate to have both fisheries and statistical experts as supervisors, who provided me with the necessary means to deepen my knowledge on fisheries management (especially within the broader context of Marine Spatial Planning (MSP)), statistics and, more than ever, programming! What’s more: for the first time I had the opportunity to deal with a broad and very rich scope of dataset. A major challenge during my PhD was to develop a statistically sound model that was able to integrate these very distinct, yet complementary, types of data. The ultimate goal was to provide more precise abundance estimates of fishery resources such that it could subsidize spatio-temporal management practices. If you fancy for more details on this, you can browse through my thesis here.

What do I do?

Broadly speaking, anything related to data science. Although my main expertise so far has been related to the analysis of ecological data (with strong focus on the spatial management of fishery resources), the statistical language across other fields are pretty much the same - the difference only being the field-specific jargons (and which are easy to adapt to).

Having dealt with data at both population and community level, including spatial and temporally explicit data, I gathered statistical expertise to address issues between both subjects. I’ve covered topics from univariate (LM, GLM, GAM, GLVM, …) to multivariate statistics (CA, (n)MDS, PCA, RDS, MGLM, MANOVA, …), and implemented those either within a Bayesian or frequentist framework. I’m particularly adept of non-linear & latent variable models within spatial and temporal contexts.

I’ve spent the bulk of my time applying and implementing these models within the R programming language, with which I feel the most confident with. Occasionally I use Python and Matlab, and more recently I’ve been digging into web-oriented programming languages such as CSS, Go, and YAML to produce this personal website.

Last but not least, as more than ever everything has become very visual in our society, I like to spend time displaying information graphically (I can spend countless hours to produce the perfect plot!). I’m most fond of mapping (when GIS data are in hand), but overall I like anything that involves visual data communication.