Leading the development of advanced AI models using Python and a variety of machine learning frameworks and tools, including PyTorch, scikit-learn, Rasterio, and Xarray. Specializing in deep convolutional neural networks (CNNs) and other machine learning algorithms to integrate geophysical and geospatial data. Driving innovation in mineral target generation and exploration through enhanced algorithm development and strategic data analysis.

Developed and implemented machine learning and deep learning methodologies in Python, integrating geophysical and geospatial data. Leveraged domain knowledge to improve feature engineering processes by a factor of 1000x, reducing processing time from approximately 25 hours to under two minutes. Enhanced data integration techniques, leading to improved algorithm performance and reliability. Focused on creating algorithms that enhanced mineral target generation and drove innovation in exploration and resource identification.

Developed novel Convolutional Neural Network (CNN) algorithms for sea ice mapping, leveraging Synthetic Aperture Radar (SAR) data and other remotely sensed imagery to accurately characterize and accelerate sea ice mapping processes using Python (including PyTorch, scikit-learn, Rasterio, Xarray, and other libraries). Mentored graduate students in their research, focusing on Geographic Information Systems (GIS) and machine learning applications. Led and participated in the writing and submission of grant proposals, successfully securing over US$400k in funding.

Developed different algorithms for machine learning for geoscience. Regularly using Python, R and Julia occasionally. Interested in the adaptation and implementation of computer vision techniques, especially convolutional neural networks, for different geoscience tasks, such as petrophysical thin section data, core images, and remote sensing data.

Conducted research on the applications of machine learning techniques for geoscience problems, including carbon sequestration and space weather projects. Performed the integration of a large variety of geoscience data using Python. The research was summarized and published into two expanded abstracts and one peer-review journal paper.

Processed seismic data from Brazil’s pre-salt. Processed one 3D seismic data from noise attenuation to post-migration using Omega, managing a pre-stack dataset with roughly 10 Tb of size. Executed the quality control of the data, often with Petrel. Recognized by local management for the fast delivery speed.

Performed the Quality Control and preprocessing of airborne magnetometric and gamma‐ray spectrometry surveys data using Geosoft’s Oasis montaj. Responsible for mobilization of three other team members, as well as daily field crew finances.

Capstone thesis: “Measurements of thermophysical properties controlled by microcomputer”Collaborated on the development of a calorimeter to measure specific heat capacity of pulverized rocks. Implemented the interface connecting the calorimeter’s multimeter to a computer using Excel’s VBA.