As a Data Scientist at Geoapp, I specialize in the application of remote sensing technologies and the analysis of Earth Observation (EO) data. My responsibilities include:* Geospatial Data Analysis: Utilizing Python to analyze geospatial data and develop predictive models for geological risk assessment.* Risk Monitoring and Prediction: Utilizing tools such as radar, inSAR, prisms, and various additional data sources to monitor and predict geological events like landslides and failures.* Software Development: Actively developing several plugins for QGIS aimed at enhancing the detection and analysis of images and remote data.* Failure Analysis: Leveraging machine learning algorithms to conduct comprehensive failure analysis, extracting valuable insights from complex datasets.This role allows me to contribute to the field of Earth Observation and address environmental and geological challenges.

As a doctoral student in biorobotics, I specialized in remote sensing and data analysis for exteroception and proprioception in soft robots. My coursework focused on statistics, deep learning, and machine learning algorithms, equipping me with the skills to tackle complex data-driven challenges. Attending the EELISA spring school broadened my knowledge in explainable AI and data management ethics.

Experienced data scientist with a strong command of the R programming language. Skilled in data analysis and visualization, utilizing tools such as R Markdown and R Shiny. Proven track record of successfully leading projects and coordinating teams, showcasing organizational and project management abilities. Contributed to the development of predictive models for industry 4.0 and healthcare applications. Collaborated with prestigious institutions on biosensor signal analysis.

Manufacture and integration of nano electronic devices based on 2D-materials on flexible substrates for wearable biosensing for infant care (https://www.wasp-project.eu/).- Printed electronics.- Inkjet printing of 2D materials inks.- Study and development of full-printed diode.Material characterization: - Electrical characterization- Morphological characterization- Raman spectroscopy.

Design and development of paper-based electrochemical biosensors for point-of-care diagnostics and wearable applications using graphene. This work involved developing a novel methodology for producing reduced-graphene oxide electrodes, which significantly enhanced its performance.- Design of an electrolyte-gated graphene FET.- Improved graphene oxide printing technology.- Printing graphene electrodes for electrochemical biosensing both on plastic (PEG and PEN) and biocompatible matrials (ie nitrocellulose paper, cotton).- Electrodes functionalization with bioreceptors (immunoglobulins, serum proteins) and inorganic nanoparticles (Au and IrOx) for the purpose of wearable in vivo sensing and heavy metal detection.Material characterization:- Electrical characterization with four point probes- Electrochemical characterization- Raman spectroscopy- SEM- TEM- AFM