Statistics and Artificial Intelligence 

Statistics is, in fact, at the inner core of the inferential processes that bring about the development of efficient machine and deep learning tools and, eventually, artificial intelligence. I was honoured to chair the Belle II statistics committee in 2019-2023. I still serve on this committee, which provides guidance to Belle II collaborators on the appropriate usage of statistical tools in physics analyses.

Inferential statistics is crucial to making predictions and generalisations based on some sample of data.

Machine learning algorithms and artificial intelligence systems are usually provided with large amounts of data. Through the implementation of algorithms based on statistical models, they learn patterns in and from the data and achieve the capability to make predictions or perform classification tasks.

The essential advantage of using machine and deep learning algorithms is their efficiency and precision in performing tasks. These algorithms often improve the sensitivity of a search, the accuracy in making a prediction, and so on.

In my work, I extensively use statistics, machine learning, deep learning, and artificial intelligence to design and implement solutions to research problems in particle physics, in gravitational waves, and in the integration of machine learning solutions directly in the hardware of the experiments.