Post-graduate researcher at Universidad Politecnica de Madrid, specializing in deep learning for computer vision. I design and implement neural networks for person detection, face recognition, and video event recognition, collaborating with industry leaders like Nokia and Airbus.
Download My CVCNNs, Transformers, Vision Transformers, LLMs, VLMs, Diffusion Models, GANs, MoEs.
PyTorch, TensorFlow, HuggingFace, OpenCV, CLIP, Stable Diffusion, Llama, Gradio.
Docker, Kubernetes, AWS SageMaker, Linux, GPU Clusters, Python, Bash, Git.
Post-graduate Researcher
Universidad Politecnica de Madrid
Deep learning for computer vision: person detection, face recognition, and video highlight detection. R&D collaboration with Nokia and Airbus.
Junior Programmer
Indra Sistemas S.A.
VoIP systems development for air traffic management. Network configuration, task automation, Scrum methodology.
Data Coder & Trainee Programmer
DEYDE Calidad de Datos S.L.
Expert systems development for postal address coding and verification. Database management.
Ph.D. in Communication Technologies and Systems
Universidad Politecnica de Madrid, UPM
Deep learning research for computer vision applications.
M.S. in Telecommunication Engineering
Universidad Politecnica de Madrid, UPM
Master's Thesis grade: Distinction (10/10)
B.S. in Telecommunication Systems Engineering
Universidad de Alcala de Henares, UAH
Bachelor's Thesis grade: Excellent (9/10)
ViMoCLIP: Video Motion Cues for Animal Action Recognition
A novel approach that augments static CLIP representations with video motion cues for improved animal action recognition. Published at IEEE/CVF CVPR Workshops 2025.
Text-Guided Sports Highlights with CLIP
CLIP-based framework for automatic video summarization of soccer matches. Uses multimodal (text + image) neural networks for highlight detection.
Vision Transformers vs CNNs for Face Recognition
Comprehensive comparison between Vision Transformers and CNNs for face recognition tasks. Published in Nature Scientific Reports.
Automatic Highlight Detection in Martial Arts Tricking
Deep learning system for automatic highlight detection in martial arts tricking videos using 2D/3D CNNs, recurrent networks and Transformers.
UPM-GTI-Face: Face Detection Dataset
Dataset for evaluating the impact of distance and face masks on face detection and recognition systems. Presented at IEEE AVSS in Madrid.
Automatic Sports Video Summarization with Identity-Aware Highlight Selection
Doctoral dissertation presenting novel deep learning methods for automatic sports video summarization. Combines identity-aware techniques with highlight detection for personalized content generation.
Automatic Highlight Detection in Martial Arts Tricking Videos
Development of a deep learning strategy to automatically detect highlights in martial arts tricking videos. Grade: Distinction (10/10).
Acoustic Bird Classification Using MFCC Feature Extraction
Acoustic classification of bird species using sound feature extraction through MFCC (Mel-Frequency Cepstral Coefficients) parameters. Grade: Excellent (9/10).
Olympus: OpenClaw Agent System
Personal AI workspace built on OpenClaw and running 24/7 on a dedicated Mac Mini. It orchestrates specialized agents for coordination, planning, and coding, with persistent memory, Telegram-based control, task tracking, and GitHub-backed automation.
Paper Copilot: Research Paper Summarizer
Local AI agent that reads a research paper (PDF) and produces a structured, Notion-ready markdown summary including metadata, methodology, key results, reference analysis charts, and extracted figures.
Job Finder: AI Job Monitoring Agent
Local-first AI agent that crawls 16+ public job sources, normalizes postings, and scores relevance using hybrid ranking (rule-based, semantic embeddings, and LLM fit). Includes a Streamlit dashboard for review.
RoboMaster Tank Detection
Designed a university course assignment using RoboMaster tanks. Implemented localization networks in TensorFlow, object detection in PyTorch, and YOLOv8 for real-time detection.
Story & Image Generation
INDESIAhack: Weather Detection for Ferrovial
Led a team to build a Model of Experts (MoE) combining CLIP, Microsoft Azure, and ChatGPT API to assess road visibility from traffic cameras worldwide. Ran on AWS SageMaker.
Custom Components with TensorFlow
Organized workshops teaching custom TensorFlow components: loss functions, activations, initializers, regularizers, metrics, layers, models and training loops. Explored library internals and graph management.
Personal Website
Self-taught HTML, CSS, and JavaScript to build this personal portfolio. Learned web development from scratch, version control with Git, and deployment on GitHub Pages.
Kubernetes Cluster with 17 GPU Nodes
Built a Kubernetes cluster integrating 17 GPU-equipped computers. Configured NVIDIA support, native authentication, NFS storage, custom Docker profiles, and a minimalist JupyterHub interface for distributed neural network training.
Multi-GPU Workstation Assembly
Selected components and assembled multiple high-performance multi-GPU workstations for the research group. Handled system administration: OS installation, user management, package control, and driver updates.
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