Special Session 9

Measurement quantities are affected by uncertain, that is, their knowledge is not deterministic. This uncertainty is unavoidable and its evaluation is fundamental for measurement since it allows to compare quantities and take decisions. Moreover the same quantities, especially the ones related to living environment are randomness, i.e. affected by stochastic variation. Probability theory and Statistics provide powerful tools for the development and analysis of new mathematical models of phenomena with random characteristics and for drawing conclusions and making inferences. Today probabilistic models are the basis of a large number of applications in modeling different fields of human activity. In particular, these methods use the language of probability to model different phenomena for the evaluation and prevision of measurements in various areas of the applied mathematics. Therefore, the aim of this session is to delineate the fundamentals of probability and mathematical statistics impressing a comprehensive understanding of the theory and mechanics of the calculations through several applications in environmental and metrology fields to improve the quality of life.

Antonella Iuliano, is a Researcher in Probability and Mathematical Statistics at the University of Basilicata, Italy. She holds a PhD in Mathematics from the University of Salerno, Italy, awarded in 2012. During this period she focused on the development of different stochastic processes with applications in the study of Birth-Death Processes and generalized Telegraph Processes. International scientific collaborations were successfully completed and published. From 2013 to 2018 she worked as a post-doctoral researcher at the IAC-CNR where she has carried out a research activity mainly focused on Applied Statistics and Computational Biology. Specifically, her research interests have been the development and use of high-dimensional statistical methods for the analysis and integration of multi-omics cancer survival data in biological and medical fields. In this period she collaborated with the Artificial Intelligence Group at the Department of Computer Science and Technology of the University of Cambridge, UK, working to various international research projects. She published her results in several conferences and journals. From 2019 to 2021, she has worked as Senior Mathematical Statistician in the Bioinformatics Core at the Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy. As responsible for the analysis of statistical data, she supported the research activity of the Institute in the study of a wide range of problems in the context of genomic and transcriptomic using specific and novel mathematical-statistical methods.

Antonio Di Crescenzo, is Full Professor at the Department of Mathematics of the University of Salerno, where he is also a member of the Advisory Board of the Graduate School in Mathematics, Physics and Applications. From September 2020 he is the head of the PRISMA Group (PRobability In Statistics, Mathematics and Applications) of the Italian Mathematical Union. He is the head of the Research Unit of the University of Salerno for the PRIN-2017 project "Stochastic Models for Complex Systems" (2019-2023) funded by MIUR. His research interests include stochastic modeling, theory and simulation of stochastic processes with applications to biomathematics, queueing systems, reliability theory, aging notions and information measures. He held various research periods abroad, mainly in Spain, Japan, USA. He is the author of about 100 publications, appearing mainly in international journals and Proceedings of International Meetings. He co-organized several International Meetings and participated in more than 60 international scientific meetings, specially in applications of probability, in biomathematics and biocybernetics. He is member of the Editorial Board of various journals, such as Methodology and Computing in Applied Probability, AIMS Mathematics, Journal of Mathematics. He is Editor-in-Chief for the Probability and Statistics Theory Section of Mathematics, MDPI.

Pietro Liò, is Full Professor at the department of Computer Science and Technology of the University of Cambridge and he is a member of the Artificial Intelligence group. He is also member of the Cambridge Centre for AI in Medicine. His research interest focuses on developing Artificial Intelligence and Computational Biology models to understand diseases complexity and address personalised and precision medicine. Current focus is on Graph Neural Network modeling. He has a MA from Cambridge, a PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and a PhD in (Theoretical) Genetics (University of Pavia, Italy). He is member of CAMBRIDGE CENTRE FOR AI IN MEDICINE - the Integrate Cancer Medicine Institute, the committee of MPhil in Computational Biology (Stakeholder Group for the CCBI) , steering committee of Cambridge BIG data, VPH-UK (Virtual Physiological Human), Fellow and member of the Council of Clare Hall College, and of Ellis, the European Lab for Learning & Intelligent Systems. Finally, he is member of the Academia Europaea. He is listed in www.topitalianscientists.org/Top_italian_scientists_VIA-Academy.aspx. He is member of Complaint Officer/Examination Review Committee (Cambridge University) and reviewer of 4 MPhils (Newcastle University), steering committee VPH-UK.