Passionate engineer with biomedical background. I'm interested about different topics
such as Data Science, AI, Software Engineering but, also Strategy and Management field applied to Technology area.
I would describe myself as a determined and enterprising person, capable of operating and collaborating in a team to achieve the established objectives.
If you are interested, click below to classic CV ⬇️
Building of semi-automatic pipeline for processing particular case of 3D-MRI images and benchmark between that method and a deep learning architecture (3D-U-Net). The reproducibility of the experiments is ensured by building a project (DBB Distorted Brain Benchmark) on the Open Science platform BrainLife.io and the containerization of pipeline using Docker and publishing a Brainlife.io app (DBB preprocessing t1w)
The project focuses on the prediction of C02 emissions in the USA using different statistical and machine learning methods. The main techniques was advanced regression methods (Lasso regression), autoregressive methods (ARIMA, GARCH, RegARIMA models) and dynamic regression methods (State-space models, Kalman filter).
This project is a Proof of Concept and is applicable to small and/or medium-sized healthcare facilities that would like to embark on a Digital Transformation journey that would lead to Continuous Monitoring and Integration of facility and (not strictly healthcare) patient/client data.
The goal of the project is the creation of a web-application for the integration of data derived from Patients through ad-hoc developed questionnaires (PREM) with the facility's operational and economic management metrics, to set up a data-driven improvement strategy
The project focuses on the development of an API for research purpose to justify our MLOps pipeline in the article: MLOps Use-Case in Biomedical field. It's an API for multi-class classification of ultrasound images. The JSON response allows to print the probability of each class.