I'm an Industrial Electronics and Automation Engineer graduated from the Polytechnic University of Valencia, and I hold a Master's in Artificial Intelligence from the International University of La Rioja. Recently, I have enrolled in a remote Computer Science degree to further expand my technical knowledge and skills.
Currently, I am actively looking for new opportunities in the field of Artificial Intelligence, Machine Learning, and Data Science, where I can apply my experience in anomaly detection, satellite image analysis, and deep learning.
I have experience developing virtual reality tools at Simumatik, contributing to the creation of virtual industrial commissioning environments. My final degree project was completed with this company, which you can check out here: Virtual Commissioning with Virtual Reality.
Additionally, my background in cybersecurity has allowed me to integrate data science and security, optimizing decision-making in complex digital environments.
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Artificial Intelligence & Machine Learning:
- Proficient in building models using Deep Learning, Transformers, RNN, and LSTM for tasks ranging from sequence modeling to object detection.
- Extensive experience in developing and optimizing machine learning pipelines for various applications, including anomaly detection, classification, and regression.
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Land Change Detection in satellite images:
- Specialized in detecting land changes in satellite imagery and environmental data using the state of the art in computer vision techniques.
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Data Science:
- Skilled in statistical analysis, data preprocessing, and feature engineering to prepare datasets for robust model development.
- Expertise in model evaluation, performance optimization, and using frameworks like scikit-learn, TensorFlow, and PyTorch to create high-performing predictive models.
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Marine Platforms:
- Experience in developing models for monitoring and anomaly detection in offshore wind farms using sensor data and dynamics simulations.
- Utilized surrogate models to simulate marine environments and detect faults or anomalies in complex marine systems.
- Developed systems for real-time anomaly detection in offshore wind farms and other marine platforms, ensuring operational efficiency and predictive maintenance.
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Programming Languages:
- Proficient in Python, MATLAB, and C++ for designing algorithms, data analysis, and implementing AI models.
- Python expertise includes popular libraries such as NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch.
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Open-Source Systems (Linux):
- Advanced knowledge of Linux operating systems for development, deployment, and system administration tasks.
- Experience in using Linux for creating automated workflows, running AI models on high-performance systems, and managing cloud or local development environments.
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Containerization & Orchestration:
- Docker: Proficient in using Docker to create containerized applications for AI/ML models, ensuring consistency across different environments.
- Kubernetes: Experience in orchestrating containerized applications using Kubernetes, deploying scalable and resilient AI systems in distributed environments.
- Implemented CI/CD pipelines with Docker and Kubernetes to streamline development and deployment processes for machine learning models.
- Building intelligent systems for anomaly detection in satellite images.
- Exploring new applications of Transformers in sequential data modeling.
- Creating datasets and models for marine environmental monitoring.
- Diving into LLM to create a very light version of ChatGPT to develop understanding on the topic
- Virtual Commissioning with Virtual Reality: Developed VR tools for industrial simulations.
- [Satellite Image Analysis for Anomaly Detection]: Built deep learning models for land change monitoring.
- [Customer Churn Prediction]: Applied ML to forecast customer churn during my Ironhack Bootcamp.
- [Movie Recommendation System]: Developed a collaborative and content-based filtering system.
Land Change Detection using Deep Learning
My Master's Thesis focused on detecting land changes using deep learning techniques applied to satellite imagery. I developed a model that identifies and classifies environmental changes over time, leveraging Convolutional Neural Networks (CNNs) and Transformers for feature extraction and anomaly detection. The study aimed to improve the accuracy of monitoring urban expansion, deforestation, and natural disasters.
- Deep Learning (CNNs, Transformers)
- Satellite Image Processing
- Python, TensorFlow, PyTorch
- Geospatial Data Analysis
- Computer Science Degree (Remote): Expanding my knowledge in software engineering, algorithms, and computer systems to complement my AI expertise.
- [Marine Wind Platform Anomaly Detection]: Built real-time models for monitoring offshore wind farms.
- [CNN-Based Image Classification]: Developed a deep learning model using CIFAR-10 dataset.
- [NLP Sentiment Analysis]: Applied natural language processing to analyze and classify sentiments.
- [Telegram Bot Development]: Created a Telegram bot for automated responses and integrations.
- [Rebranding of Transportes Fernández]: Developed a complete rebranding project, including a fully functional React-based website.
- [Containerized Applications with Docker]: Series of projects focusing on containerization, improving deployment and scalability using Docker and Kubernetes.
Feel free to reach out through.
