Senior Researcher
Phone: +39 091 6809 464
Email: antonio.pagliaro@inaf.it
Google Scholar · Personal Website
Academic Editor and Section Board Member at Applied Sciences (MDPI).
Research Interests
Artificial Intelligence · Machine and Deep Learning · Computer Vision · High Energy Astrophysics · Data Driven Finance · Explainable AI (xAI)
My research aims to achieve greater generality in artificial intelligence: empower computers to perform advanced tasks — learning from experience, adapting seamlessly, and discovering new knowledge — primarily in high energy astrophysics and astroparticle physics, but also in quantitative finance and computer vision. As data volumes and complexity grow, we need machines that extract meaningful patterns from vast amounts of information while presenting their findings in ways humans can comprehend.
Recent AI Research Papers
High Energy Astrophysics
- Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis — cover story, Appl. Sci. Vol. 15(7)
- Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
- Application of Machine and Deep Learning Methods to the Analysis of IACTs Data
Data Driven Finance
- Regime-Aware LightGBM for Stock Market Forecasting: A Validated Walk-Forward Framework with Statistical Rigor and Explainable AI Analysis
- Cognitive Biases in Asset Pricing: An Empirical Analysis of the Alphabet Effect and Ticker Fluency in the US Market
- Artificial Intelligence vs. Efficient Markets: A Critical Reassessment of Predictive Models in the Big Data Era
- Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads
- An Introduction to Machine Learning Methods for Fraud Detection
Computer Vision & More
- An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
- Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
- AI in Experiments: Present Status and Future Prospects
- The Specialization of Intelligence in AI Horizons: Present Status and Visions for the Next Era
Projects
- HPC, Big Data and Quantum Computing — National Centre
- MADELEinE — Machine and Deep Learning in Experiments
- ASTRI — Imaging Atmospheric Cherenkov Telescopes (WP: Data Processing, Software Reconstruction)
- CTA — Cherenkov Telescope Array
Research Areas
Machine Learning & Deep Learning
New algorithms and techniques for supervised, unsupervised, and semi-supervised learning, as well as deep learning methods for large-scale and complex data.
Computer Vision
Methods for image and video analysis, including object detection, image segmentation, and image captioning.
Data Mining
Extracting useful information and knowledge from large and complex data sets: clustering, classification, and association rule mining.
Predictive Modeling
Methods for prediction and forecasting in astrophysics and quantitative finance.