The outbreak of the Covid-19 epidemics in early 2020 has caused an unprecedented effort of the scientific community to produce models that could monitor and
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Category: Milano
The Experts Method for the prediction of big data streams of energy flow
We have developed a method, called Experts Method, to forecast the evolution of a multivariate set of time series, of big dimension, and with partially
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BIGMATH: Stochastic Geometric modelling and 3D image analysis for human face prostheses
My name is Filipa Valdeira, I am from Lisbon (Portugal) where I have completed an Integrated Master in Aerospace Engineering, with a specialization in Systems
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BIGMATH: Mathematical morphology for the prediction of face expression transition
My name is Rongjiao Ji, I am currently a first-year PhD student in the PhD school in Mathematical Sciences, Universita’ degli Studi di Milano (Italy),
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The new Data Science Research Center at University of Milan
We are happy to announce the birth of the Data Science Research Center, a new coordinated research center of Università degli Studi di Milano. The
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Comparison of statistical techniques for the analysis of ”omics” data
This report refers to a project which has been assigned to a group of students in the MSc in Mathematics at Università degli Studi di
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A Markov Chain approach to evaluate the impact of CAP Greening on land use in Lombardy region
A recent reform has redesigned Common Agricultural Policy (CAP) contents over the programming period 2015-2020, introducing important changes in the funding strategies, called “Greening”, which
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Clustering of multivariate Big Data
Clustering is the division of a collection of data into groups, or clusters, such that points in the same cluster have a small distance from
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Inferential and Clustering Methods for Functional Data
Author: Andrea Ghiglietti Position: Postdoctoral Researcher Affiliation: Department of Mathematics, Università degli Studi di Milano. E-mail address: andrea.ghiglietti@unimi.it In the last two decades, there has
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Mathematical and numerical modeling of the cardiac electro-mechanical coupling
Aim. Our goal is to develop effective computational tools to push forward the understanding of the fundamental mechanisms underlying the origin of life-threatening arrhythmias and
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