Antonietta Mira, Professor of Statistics at Università della Svizzera italiana and professor at Insubria University, Como, Italy

Some genes are differentially expressed in patients when compared to controls. Identifying them is of paramount importance for diagnosis and for identifying possible treatments. The Intrinsic Dimension (ID) is a novel methodology that allows the identification of differentially expressed genes whilst, simultaneously, quantifying the uncertainty of the resulting non-linear clustering of patients. We have used the same methodology to study when a protein is in a folded versus unfolded state thus allowing us to better understand its properties and functionality: failure to fold into a correct threedimensional structure can produce inactive or even toxicproteins. Functional magnetic resonance imaging (fMRI) measures brain activity by detecting changes associated with blood flow. By analysing fMRI data using the ID we better understand when an area of the brain is in use because blood flow to that region increases. Using statistical models, we design risk maps of out of hospital cardiac arrests and help with optimal positioning new defibrillators or relocating existing ones. Finally, I will present research on the evolution of biodiversity of the Great Barrier Reef and on clustering countries based on COVID-19 data dynamics. All these examples share an interdisciplinary flavour and highlight the ability of proper statistical tools to improve our understanding of the world when applied to complex dataset - making our lives better