Vincenzo Piuri
Introduction
My research:
artificial intelligence, machine learning, deep learning, transfer learning, explainable AI, signal and image processing, intelligent systems, industrial and environmental applications
My expertise is:
artificial intelligence - scientific and application aspects in scientific and technological areas
A problem I’m grappling with:
how to use artificial intelligence, machine learning, signal and image processing, instrumentation and measurement to address specific problems in physics
Projects
Explainable AI for Online and Transferable Learning
Develop XAI techniques for online and transfer learning applied to experimental data from physics and signal and image understanding; handle efficiently real-time massive sensor data in online and transfer learning; develop XAI techniques for high robustness and accuracy in multi-sensor environments.
General searches with (GNN) AutoEncoders
Develop general Graph Neural Network (GNN) reconstruction algorithm(s) for all event types in experiment; based on this GNN architecture, use Variable AutoEncoder (VAE) to encode the dataset in a latent space (LS); apply the VAE to simulated data, also including theoretically motivated but unobserved physics signatures.
Physics Model-Based AI for Rare Events
Develop a novel strategy to perform reliable extrapolation with machine learning (ML) by space-unwrapping; efficiently handle extreme value modelling via machine learning using model-based strategies like exponentially tiled estimators; solve stochastic partial differential equations via physics informed neural networks for transport equations.
