I graduated from the Physics Department at the University of Calgary. With over 10 years of satellite data, I have studied ionospheric turbulence and irregularities that affect GNSS signals, particularly during solar maxima. My research includes analyzing wave phenomena in the equatorial ionosphere, for which I combined various Swarm data sets and employed advanced data analysis techniques. Additionally, I have investigated complexity and turbulence intermittency in ionospheric irregularities, demonstrating a potential Self-Organized Criticality (SOC) in these irregularities. I also used regression machine learning models to predict neutral wind speed, enhancing our understanding of space weather.

During my postdoctoral studies, I am focusing on Fourier Neural Operators (FNO) to solve partial differential equations using cutting-edge machine learning techniques. I aim to develop 2D and 3D models of geoelectric resistivity, with the primary goal of studying geophysical systems through much faster Bayesian inversion.

In the M2Pi program, I studied the Canada House of Commons to rank Canadian legislators using graph theory based on text analysis. My passion lies in working with diverse data types (time series, images, texts) to uncover hidden information, predict system behaviour, and provide insights for forecasting or managing physical, socio-political, and socio-economic systems.

All in all, I am passionate about working with different types of data (time series, images, texts) performing different analysis tests to extract hidden information or predict the next steps of a system or providing information about current an next steps of static or dynamic systems to forecast or manage a physical or sociopolitical and socioeconomic systems.