Associate Professor at the Technical University of Valencia (UPV) and former Scientific Project Officer at the Joint Research Centre, European Commission. Part of VRAIN, ELP and the DMIP Team, and Director of the NEW Master in AI & Big Data Analytics at the UPV. I hold a B.Sc. in Computer Science, an M.Sc. in Software Engineering, Formal Methods and Information Systems, a Post-Graduate Diploma in Engineering Business Management and a Ph.D in Computer Science from UPV. The evaluation and measurement of intelligent systems is, among other more applied research topics, the main scientific-technical objective of my research agenda.
I co-edit the AI Evaluation Digest, a newsletter on AI evaluation research.
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2026
Paper accepted: “A Rising Tide: Revisiting the Occupational Impact of AI in the Generative Era”
Two papers accepted at DS 2026 and IBERAMIA 2026
“Size-Constrained Clustering via Bat Algorithm Optimization” (DS 2026) and “GClus: Enforcing Explicit Size Constraints in Graph Clustering through Structural Refinement” (IBERAMIA 2026).
Conference
Three papers accepted at ECML-PKDD 2026, plus two workshop papers at QCDS 2026 and SoGood 2026
“What Should an AI Assessor Optimise for?” (Machine Learning Journal track), “Targeted Robustness for Regression via Top-Feature Noise” (Research track), “CapFlex: Multimodal Soft Clustering with Flexible Size Constraints” (Demo track), “Pre-Generation LLM Failure Prediction Under Distribution Shift” (QCDS 2026) and “The Greenest Token Is the One You Never Generate: Pre-Generation Abstention for Sustainable LLMs” (SoGood 2026).
Conference
Visiting Scholar at the Leverhulme Centre for the Future of Intelligence
Research stayUniversity of Cambridge (June - July)
Paper published: “General Scales Unlock AI Evaluation with Explanatory and Predictive Power”
JournalNature
Paper published: “Predictable Artificial Intelligence”
JournalArtificial Intelligence Journal
Paper published: “From Formal to Informal: How Register Shift Confuses LLMs in Spanish Dialect Recognition”
JournalInternational Journal of Data Science and Analytics
Paper accepted: “Semi-Supervised Soft Clustering with Flexible Cardinality”
ConferenceICPR 2026
Paper published: “Robustness under noise: assessing the impact of perturbed key attributes on machine learning models”
JournalInternational Journal of Data Science and Analytics
2025
Two reports published on categorising general-purpose AI models under the 🇪🇺 AI Act
“A Framework for General-Purpose AI Model Categorisation” and “A Framework to Categorise Modified General-Purpose AI Models as New Models Based on Behavioural Changes”.
ReportEuropean Commission JRC
Three papers published on electrified and autonomous vehicles 🚗🔌
“Comparative analysis of public and expert perceptions of electrified vehicles in the European Union” (Nature Scientific Reports), “Human or Machine: A novel deep learning framework for autonomous driver identification based on vehicle trajectories” (IEEE Transactions on Intelligent Transportation Systems) and “Follow the Leader: A Deep Reinforcement Learning Framework for Safe and Efficient Autonomous Car-Following” (Taylor & Francis Intelligent Transportation Systems).
Journal
Paper accepted: “A Framework for the Categorisation of General-Purpose AI Models under the EU AI Act”
Paper accepted: “Contamination Budget: Trade-offs between Breadth, Depth and Difficulty”
ConferenceIJCAI 2025
Paper accepted: “PredictaBoard: Benchmarking LLM Score Predictability” 🥇
ConferenceACL 2025 Findings
Two papers accepted at MDAI 2025 and DATA 2025
“Refining Community Detection in Social Networks: Agglomerative and Divisive Methods with Size Constraints” (MDAI 2025) and “ClustSize: An Algorithmic Framework for Size-Constrained Clustering” (DATA 2025).
Conference
New preprints on AI evaluation
“General Scales Unlock AI Evaluation with Explanatory and Predictive Power” (arXiv, thread) and “What should an AI assessor optimise for?” (arXiv).
Preprint
Paper published: “Analysing the Predictability of Language Model Performance”
JournalACM TIST
2024
Paper published: “Scaled-up, Shaped-up, but Letting Down? Reliability Fluctuations of Large Language Model Families”
JournalNature
Paper published: “A General Supply-Inspect Cost Framework to Regulate the Reliability-Usability Trade-Offs for Few-Shot Inference”
Two papers accepted
“Distilling the Effects of Language Model Contamination” and “Language Task Difficulty Prediction through LLM-Annotated Meta-Features”.
ConferenceECAI 2024
Three papers accepted
ConferenceIDEAL 2024
Area Chair
Join us in Santiago de Compostela!
ServiceECAI 2024
Paper presented: “Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models”
ConferenceAAAI 2024
Two papers published in MLST and Intelligent Data Analysis
“Unveiling the Robustness of Machine Learning Families” (MLST) and “Cracking black-box models: Revealing hidden machine learning techniques behind their predictions” (IDA).
Journal
Member of the OECD Expert Group on AI Futures
Service
2023
Paper accepted: “Adversarial Benchmark Evaluation Rectified by Controlling for Difficulty”
ConferenceECAI 2023
Paper published: “Rethink reporting of evaluation results in AI”
JournalScience
Co-organising the workshop
OrganisationPredictable AI
Two papers accepted in JAIR and Sustainable Computing
“Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models” (JAIR) and “Trends in AI Inference Energy Consumption: Beyond the Performance-vs-Parameter Laws of Deep Learning” (SUSCOM).
Journal
Paper published: “Can language models automate data wrangling?”
JournalMachine Learning Journal
2022
New preprint: “Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models”
PreprintBIG-bench
Report published: “Glossary of human-centric artificial intelligence”
ReportEuropean Commission
Two papers accepted
“Training on the Test Set: Mapping the System-Problem Space in AI” (Blue Sky Idea Runner-Up Award) and “When AI Difficulty is Easy: The Explanatory Power of Predicting IRT Difficulty”.
ConferenceAAAI 2022
2021
Five papers published on AI benchmarks, generality, fairness and impact
“Research community dynamics behind popular AI benchmarks” (Nature Machine Intelligence), “General intelligence disentangled via a generality metric for natural and artificial intelligence” (Nature Scientific Reports), “Missing the missing values: The ugly duckling of fairness in machine learning” (International Journal of Intelligent Systems), “Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks” (JAIR) and “Futures of Artificial Intelligence through Technology Readiness Levels” (Telematics and Informatics).
Journal
Excellence Award: Best Young Researcher
AwardEuropean Commission JRC
2020
Co-organising the 1st Workshop on Evaluating Progress in AI (EPAI 2020)
OrganisationECAI 2020
Three papers accepted
“Tracking AI: The Capability is (Not) Near”, “AI Paradigms and AI Safety: Mapping Artefacts and Techniques to Safety Issues” and “Family and Prejudice: A Behavioural Taxonomy of Machine Learning Techniques”.
ConferenceECAI 2020
Two papers published in IEEE TKDE and IEEE Transactions on Games
“CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories” (IEEE TKDE) and “Dual Indicators to Analyse AI Benchmarks: Difficulty, Discrimination, Ability and Generality” (IEEE Transactions on Games).
Journal
Paper accepted: “Does AI Qualify for the Job? A Bidirectional Model Mapping Labour and AI Intensities”
ConferenceAIES 2020
2019
Paper published: “Item Response Theory in AI: Analysing Machine Learning Classifiers at the Instance Level”
JournalArtificial Intelligence Journal
Two papers accepted
“Automated Data Transformation with Inductive Programming and Dynamic Background Knowledge” and “ABK-ADAPT: Dynamic Background Knowledge for Automating Data Transformation”.
ConferenceECML-PKDD 2019