Discover our International Masters in Big Data for Supply Chain and Big Data for Healthcare and Biotech

May

2021

Start: May 20 | 12:30 pm

End : May 20 | 02:30 pm

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Via Lambruschini , 4C 20156 Milano MI

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Get a preview of the study contents and join two dedicated online masterclasses

On Thursday, May 20^th at 12.30 PM CEST, join our online event dedicated to MIP’s new International Master in Big Data for Supply Chain Analytics and the International Master in Big Data for Healthcare and Biotech.

During the presentation you will have the opportunity to participate in the dedicated online masterclasses as well as learn more about the study content of both programmes:

The impact of Big Data Analytics in supply chain management: opportunities and challenges;

Supply Chain Management is facing great challenges in terms of transformation, and now more than ever, data is the key asset for managers working in this area as it is relied on for certain activities such as: designing networks, optimising inventory and distribution decisions, controlling illegal trade, forecasting demand, and preventing supply chain disruptions. The aim of the Master is to investigate how data can be beneficial for the SCM.

The growing role of Big Data in healthcare and Biotech: current trends and future perspectives;

In relation to the recent pandemic but also in general, the healthcare system relies heavily on the use of data; healthcare delivery needs to adopt a more predictive, personalised and participative model. This unique Master will focus on educating the leaders of the future to manage a paramount transformation in healthcare, moving towards evidence-based decision-making.

The programme directors and Ms. Maiocchi will be happy to answer your questions in real time and to provide you with further information about the application, the selection process and the contributions available for these programmes.

Event schedule:

12:30 pm: Welcome
12:35 pm: Masterclass “The impact of Big Data Analytics in supply chain management: opportunities and challenges”, held by Prof. Giovanni Miragliotta
1:20 pm: Presentation of the two Masters and admission requirements
1:30 pm: Masterclass “The growing role of Big Data in healthcare and Biotech: current trends and future perspectives”, held by Prof. Francesca Ieva
2:15 pm: Q&A

About the Speakers

Giovanni Miragliotta, Associate Professor – Politecnico di Milano – Big Data for Supply Chain Analytics

Born in 1973, he graduated in Management Engineering, cum laude, in 1998 at Politecnico di Milano, with a Master Thesis on “Strategic Stocks sizing through uncertainty deployment”. After a visiting period at UCLA, he attained his PhD in Management Engineering in 2003, from Politecnico di Milano, with a PhD dissertation on “Techniques and tools for multisite production management”. Currently he is Associate Professor, teaching Industrial Plants Design and Advanced Supply Chain Planning, respectively to BS and MS students. He is a core faculty member of MIP, co-director of three Observatories (Internet of Things, Industry 4.0, Artificial Intelligence) and co-director of the IoTLab.

Francesca Ieva Associate Professor – Politecnico di Milano – Big Data for Healthcare and Biotech

Francesca Ieva was born in Milan, Italy, in 1984. In 2008 she was awarded a Master Degree in Mathematical Engineering, and in 2012 a PhD in Mathematical Models and Methods for Engineering from Politecnico di Milano. Her PhD thesis received the accolade of best PhD thesis in applied statistics from the Italian Statistical Society in 2014. She became Assistant Professor in Probability and Statistics at the Department of Mathematics of Università degli Studi di Milano. From 2016 to 2019 she has been tenured assistant professor of Statistics at MOX, the modelling and scientific computing lab within the Department of Mathematics of Politecnico di Milano, where she is currently serving as associate professor of Statistics. Her research interests focus on statistical learning in a biomedical context, with special attention to applications in biostatistics, pharmacoepidemiology and healthcare assessment based on public health databases. Methodologically speaking, her research is focused on nonparametric frailty Multi State Models, Multilevel models and Bayesian nonparametric hierarchical models, Healthcare assessment, Functional Data Analysis, Depth Measures and Machine Learning for Health Analytics.