As a physics graduate, I have experience in performing data analysis in a wide range of fields; particle physics, astrophysics, optics, materials science, chemical physics, quantum physics...
The aspiration for mastering state-of-the-art techniques motivated me to initiate my first ML application as my bachelor thesis, and I've been hooked ever since.
I have passion for diverse subjects (reinforcement learning, algorithmic trading, sports analytics, etc.), and I'm currently looking for enhancing my skills in data science, software development and AI/ML.
When I'm not learning, I enjoy running and playing the piano.
The Standard Model of particle physics, while being able to make accurate predictions, has been proved to fail to explain various phenomena, such as astronomical dark matter observations. In this project, a machine learning application is implemented with the goal of studying dark matter candidates. Images from Charge Coupled Devices (CCDs) in different experiments located underground are used to test different deep learning algorithms. A U-Net model is trained with Python's open-source library Keras. The model performs multi-class image segmentation in order to detect dark matter particle signals among background noise.
Original title: Application of deep learning techniques to images collected with Charge Coupled Devices to search for Dark Matter.More information
Image recognition implementation with Keras. A CNN is built and trained with the CIFAR-10 dataset. Two models are trained: one without data-augmentation (77.25% accuracy) and the other with data-augmentation (78.04% accuracy).More information
A collection of games where agents are trained with Reinforcement Learning.More information
Small projects that I work on during my free time.More information
CHEQUE - Internship
Bachelor of Science in Physics
Bachelor of Science in Physics / Erasmus+
Native proficiency: CPE Cambridge C2
Intermediate: EOIDNA B1