I am a Machine Learning Engineer at Statsbomb. I love creating systems based on data that can interact with the real world. My research interests lie in the general area of machine learning, particularly in deep learning and its applications in object detection, image generation and instance segmentation.
Prior to coming to Statsbomb, I worked at Gradiant as a Machine Learning Research Engineer and at Desigual Headquarters as a Data Science consultant. My academic background includes a M.Sc. in Artificial Intelligence, which I completed over two years - one in Barcelona and one at Purdue University in the United States I spent one year in Barcelona and one year in the United States (Purdue University) to do a M.Sc. in Artificial Intelligence. I earned my B.Sc. from the University of A Coruña, which included an enriching exchange year in Norway.
August 2024. Statsbomb have been acquired by Hudl! I am excited to continue working on the future of sports analytics with the Hudl team.
September 2023. Published a blog post about homography estimation in Statsbomb blog [English, Spanish].
February 2022. I am joining Statsbomb as a Machine Learning Engineer .
April 2021. Drone vs Bird paper is out! Check our awarded solution.
February 2021. We have open sourced Pyodi our Python Object Detecion Insights library.
September 2020. We have won 2020 Drone vs Bird Detection Challenge.
September 2019. Paper from IEEE AVSS2019 Drone-vs-Bird Detection Challenge is out.
Comparing MOTA, IDF1, and HOTA for Multi-Object Tracking Evaluation
Understand and visualize how Kubernetes HPA works with a real world example
A comparison of the latest research and innovations in Video Instance Segmentation
A guide for your Deep Learning environment setup
A Computer Vision webapp to map NFL game images to their real-world coordinates
Streamline Your ML Workflows: A Simple Guide to MLFlow Deployment on AWS
Research and implementation of Deep Learning based models for object detection and semantic segmentation. Optimization of these solutions for being embedded on UAVs (Nvidia Jetson) or deployed in the cloud. Leadership, coordination and development of the teams AI methodologies and infrastructure.
Developed machine learning models for analyzing stock distribution and sales / returns forecasting. Research on using CNN for database image retrieval and user recommendations.
I was a member of Purdue Datalab and collaborated with PhD students by presenting, exploring and discussing new topics on AI research. Also took data mining, machine learning courses and finish my Master Thesis: Feature construction and classification on Time Series.
This was the moment when my adventure with AI began. I remember being totally impressed with the CNNs of the moment! I took courses that were mainly related with supervised / unsupervised learning approaches.
I got awarded with Erasmus and NILS scholarship. I did my final project with three ingredients: Optical Flow, a GoPro camera and my own bike.
This is where I learned about data structures, algorithm complexity and so many other things that I use so often that still surprise myself from time to time.
Sensors: Special Issue Deep Learning Based UAV Detection, Classification, and Tracking
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
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