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. I spent one year in Barcelona and one year in the United States (Purdue University) to do a M.Sc. in Artificial Intelligence. I got my B.Sc. from University of A Coruña after an exchange year in Norway.
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 Drone vs Bird Detection Challenge. More info here.
September 2019. Paper from IEEE AVSS2019 Drone-vs-Bird Detection Challenge is out.
A simple explanation and visualization of ViT attentions and ResNet activations
Code your own scripts in Python, deploy them using Ansible & Conda and get real time reports from your server
A guide to train, monitor, compare and evaluate your pytorch object detection models
We can finally use Docker buildx cache-to gha with build-push action and it is blazingly fast!
Use DVC and Git for tracking changes on your machine learning dataset
Use Github Actions cache and Docker to reduce time installing Conda dependencies
Scrap data and train a model
Learn to generate your own footbal players
Learn how to code a Variational Autoencoder
Learn the math
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
Collaborated with Federal Aviation Administration (FAA) in PEGASAS project with the aim of enhance general aviation safety. Developed a model which could classify flights in different categories (using data obtained from airborne sensors) through an unsupervised learning stage and an active learning procedure. Tools: Python, Scikit-Learn, Pandas, C, OpenMPI, Java
Presented in PEGASAS 2017 meeting at Texas A&M University
Tags: #machine-learning #scikit-learn
System for musical instrument recognition using NNs with over 10000 audio samples based on Fourier Transform input features. Created our own NN library in Python from scratch.
Tags: #python #numpy #fftransform
Analysis using Twitter public API where we focused in understanding how politicians relate in the social network. We performed a Monte Carlo simulation of corruption behavior spreading with the aim of observing how corruption flows starting from some seed users that were imprisoned or involved in some case.
Tags: #twitter #python #numpy
Developed Android app for collecting AHRS data during flights to facilitate the creation of a dataset for future experiments. This app was used by pilots and students that flew from Purdue’s airport.
Tags: #java #android #sensors