Exciting milestone! 🚀 Proud to see our pioneering autism screening tool getting the backing it deserves. The Principality of Asturias (@AsturiasGob) is funding our project to bring this innovative technology to life.
Check out the details here: https://t.co/TPe3aoDkr5
We are thrilled to be at the Avilés Summer Courses in La Granja, showcasing our groundbreaking autism screening technology for babies and toddlers.
Empowering early detection to change lives.
Thrilled to share that our paper "PyWIB: A Python Library for a Multi-Modal Approach to Web Interaction Behavior Analysis" won the Best Paper Award at the 2026 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications, held in Nice, France ! 🎉
It was a true honor to meet with Sir Simon Baron-Cohen, a world authority on autism, and his team during our visit to Cambridge. We shared our ASD detection research and are incredibly grateful for their attention. Excited for potential synergies ahead!
We present a CNN + RNN model to detect cheating in online programming exams via screenshot sequences. It achieves 95.18% accuracy & 94.2% F2-score. Data augmentation + class weights improved results; transfer learning & alt. loss functions gave no gains.
Our paper “A machine learning assistant for detecting fraudulent activities in synchronous online programming exams” has just been published at https://t.co/Poxu1PiY0o. A pleasure to work with Alonso Gago, @jqrg, and @miguelgrdotcom.
At #AIH2025 (Jesus College, Cambridge) we presented our paper on an AI system using eye-tracking + ML for early screening of Autism Spectrum Disorder (ASD).
Proud to contribute to advancing healthcare with AI!
#HealthcareAI#AutismResearch#MachineLearning
Just published our latest research on early screening for Autism Spectrum Disorder (ASD) using Artificial Intelligence.
Full paper here: https://t.co/MlbSuLJtF0
PlangRec is a character-level deep-learning model that predicts the language of a line of code. It provides 95% accuracy and macro F1-score to classify 21 different languages, outperforming state-of-the-art systems for classifying code snippets.
https://t.co/EB3skO58KE
Excited to share that our paper “PLangRec: Deep-learning model to predict the programming language from a single line of code” will be published in Future Generation Computer Systems! 🎉 Coauthored with Oscar Rodriguez-Prieto & Alejandro Pato. Try it here: https://t.co/GR4N1ctuMf