Valter Laino

Papers

Total Citations

H-Index

About

Valter Laino is a researcher at the forefront of artificial intelligence and measurement science, with a particular focus on the reliability and trustworthiness of neural network-based systems. His most cited work, "A Survey on Uncertainty Assessment in ANN-Based Measurements" (2023), has already garnered 13 citations, underscoring its timely impact. In this landmark paper, Laino addresses a critical gap in the AI landscape: as neural networks become ubiquitous in both research and industrial applications, the need for rigorous uncertainty quantification grows ever more urgent. By systematically reviewing methods to assess and communicate the confidence of AI predictions, he provides a foundational framework for engineers and scientists deploying these models in high-stakes environments. His contributions bridge the gap between theoretical AI and practical metrology, ensuring that measurements derived from artificial neural networks are not only powerful but also dependable. Laino’s work is essential reading for anyone seeking to understand how to make AI systems more transparent and robust, positioning him as a key voice in the ongoing effort to build trustworthy intelligent systems.