Bio
I am a Machine Learning Engineer at StatsBomb, focused on developing cutting-edge computer vision systems for sports analytics. My work involves developing production-scale AI systems for real-time analytics, with expertise in object detection, instance segmentation, and generative models. I'm passionate about creating AI systems that bridge the gap between complex data and real-world applications.
My professional journey includes leading computer vision research at Gradiant and implementing machine learning solutions for retail optimization at Desigual Headquarters. I completed my M.Sc. in Artificial Intelligence through a dual program, studying at the Polytechnic University of Catalonia for the first year and Purdue University for my final year. I hold a B.Sc. in Computer Science from the University of A Coruña, which included an enriching exchange year at the University of Stavanger in Norway.
News
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August 2024. Statsbomb have been acquired by Hudl! I am excited to continue working on the future of sports analytics with the Hudl team.
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September 2023. Published a blog post about homography estimation in Statsbomb blog [English, Spanish].
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February 2022. I am joining Statsbomb as a Machine Learning Engineer .
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April 2021. Drone vs Bird paper is out! Check our awarded solution.
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February 2021. We have open sourced Pyodi our Python Object Detecion Insights library.
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September 2020. We have won 2020 Drone vs Bird Detection Challenge.
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September 2019. Paper from IEEE AVSS2019 Drone-vs-Bird Detection Challenge is out.
Posts
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Httpx vs Aiohttp: Handling High-Concurrency Requests in Async Python
How switching from httpx to aiohttp resolved my httpx read errors and networking issues
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Understanding Object Tracking Metrics
Comparing MOTA, IDF1, and HOTA for Multi-Object Tracking Evaluation
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A Guide to Horizontal Pod Autoscaler
Understand and visualize how Kubernetes HPA works with a real world example
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Exploring Transformer-Based Methods in Video Instance Segmentation
A comparison of the latest research and innovations in Video Instance Segmentation
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Building an Efficient Docker Image with Poetry and PyTorch
A guide for your Deep Learning environment setup
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NFL Field Mapping: A Journey Through Sports Analytics and Homography
A Computer Vision webapp to map NFL game images to their real-world coordinates
See more posts
Vitæ
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Vigo, ESP (Remote)Feb'22 - now
Machine Learning Engineer - Statsbomb
Architecting and deploying advanced computer vision systems for automated sports analytics, processing thousands of matches across 90+ leagues. Building custom deep learning models (PyTorch) for real-time player tracking, ball detection, and camera calibration. Established an end-to-end MLOps pipeline for model training, deployment, and monitoring at scale, while leading AI infrastructure modernization and technical initiatives in sports data collection.
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Vigo, ESPApr'19 - Jan'22
Senior Machine Learning Research Engineer - Gradiant
Led development of computer vision solutions for real-time object detection and semantic segmentation with deep learning frameworks (PyTorch/TensorFlow). Optimized models for deployment on edge (Nvidia Jetson) and cloud platforms (AWS/Azure). Directed AI best practices, MLOps pipelines, and infrastructure modernization.
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Barcelona, ESPFeb'18 - Apr'19
Data Scientist, Consultant - Desigual, InnoIT
Designed end-to-end machine learning solutions for retail optimization, including demand forecasting and intelligent inventory management across a global store network. Developed a CNN-based visual search system for product recommendations and style matching. Implemented predictive analytics to enhance stock distribution and improve supply chain efficiency.
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Indiana, USAAug'16 - Jun'17
Master in Artificial Intelligence (Study Abroad) - Purdue University
Researcher at Purdue Datalab, collaborating with PhD candidates on AI projects. Specialized in time series analysis and feature engineering, with thesis: Feature Construction and Classification of Time Series Data. Completed advanced coursework in machine learning and data mining.
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Barcelona, ESPSep'15 - Jun'17
Master in Artificial Intelligence - Polytechnic University of Catalonia
Specialized in computer vision and neural network architectures, with focus on supervised and unsupervised learning methodologies. Core coursework included deep learning, statistical modeling, and pattern recognition. Developed strong foundation in fundamental AI algorithms and their practical applications.
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Stavanger, NORAug'14 - Jun'14
B.Sc. Student (Study Abroad) - University of Stavanger
Awarded competitive Erasmus and NILS scholarships for international studies. Completed thesis project on computer vision applications, implementing optical flow algorithms for motion analysis using action camera data. Focus on real-time video processing and computer vision techniques.
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A Coruña, ESPSep'11 - Jun'16
B.Sc. Student - University of A Coruña
Comprehensive foundation in computer science fundamentals, with focus on algorithmic complexity, data structures, and software development methodologies.
Publications
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Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
Sensors: Special Issue Deep Learning Based UAV Detection, Classification, and Tracking
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Drone-vs-Bird Detection Challenge at IEEE AVSS2019
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Other Projects
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Pyodi: Python Object Detection Insights
A simple tool for explore your object detection dataset. The goal of this library is to provide simple and intuitive visualizations from your dataset and automatically find the best parameters for generating a specific grid of anchors that can fit you data characteristics
Tags: #deep-learning #computer-vision #object-detection #pytorch