José Carlos Fontanesi Kling

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Mathematician/Programmer

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About me

I graduated in Mathematics at the Institute of Mathematics and Computer Sciences (ICMC-USP), where I focused especially on Topology and Set Theory. During my Masters I studied applications of ultrafilters (a set theoretic construct) to Dynamical Systems.

I defended my Masters in the beginning of the pandemic, and had plans to move to Germany. I decided to learn how to program, as this could open many doors and having mathematical background would be an asset.

Since then I am leaning more and more towards applied mathematics and computer sciences, and so far this has payed off, as I got a PhD position in an interesting project, in which I need to develop statistical tools to investigate possible links between climate and volcanism.

You can find more details below about my work so far or check my github to see some small and fun side projects I developed. You can also check CV (English and Deutsch).

Experience

Ash layers from volcanic eruptions can be found on land or in sediments retrieved from the ocean floor. For on land layers it is usually possible to determine which volcano is its source, while for the ones in ocean sediments multiple nearby volcanoes could be the source. This project aims to apply machine learning models to determine the source volcanoes of ash layers based on their chemical composition, using the data from on land samples as the ground truth.

This project originated from observations of coincidences between glacial/interglacial periods and volcanism. Some works detected an increase in eruption rate following the last glacial age (Watt et. al (2013), Praetorius et al. (2016)), while others observed coincidences in the periodicities present in climate records (the δ¹⁸O benthic stack compiled in Lisiecki and Raymo (2005)) and volcanism (Schindlbeck et. al (2018), Kutterolf et. al (2013)).

Analysing this data, however, is not so straightforward. While climate proxies are standard continuous time series, eruption records are points in time that can be represented as a binary time series, with ones representing instants when an event occurred. My goal in this project is to research and develop statistical tools to help determine if there is a correlation between climate and volcanism.

During this research we realized that a statiscally sound goodness-of-fit test for point processes was lacking in the literature, therefore we developed the theory and implemented such a procedure to be used in out analyses.

For more on this, check the posters available below and the Julia package with the implementation of the goodness-of-fit test.

Education

Publications

Academic events

Scholarships

Supervisor: Leandro Fiorini Aurichi

Project: Applications of ultrafilters to Ergodic Ramsey Theory (in Portuguese)

Supervisor: Leandro Fiorini Aurichi

Project: Introduction to Topological Games

Teaching

Awards

Presentations

Skills

Languages