- Associate Head of Research centre, Health Data Science
- Associate Professor of Statistics at MOX - Modeling and Scientific Computing laboratory, Department of Mathematics, Politecnico di Milano, Health Data Science
Francesca Ieva is an associate professor of Statistics at Politecnico di Milano. She got the PhD in Mathematical Models and Methods for Engineering at Politecnico in 2012, then she was hosted by the MRC Biostatistic Unit in Cambridge before becoming a junior researcher at Università statale di Milano (Department of Mathematics) in 2013 and a tenure track professor at MOX – Modelling and Scientific Computing lab, within the Department of Mathematics of Politecnico di Milano, in 2016.
Her research activity has been always focused on statistical learning in biomedical context, both from a methodological and applied point of view. In particular, she deals with health analytics for complex data in medicine.
Her main contributions regard the application of Functional Data Analysis, Survival models, Artificial Intelligence and Nonparametric Statistics to administrative data, electronic health records, medical imaging and genomic data, with the aim of defining suitable patient representation to inform predictive models for personalized medicine.
- 10/2021 - IEEE
EEG is a non-invasive powerful system that finds applications in several domains and research areas. Most EEG systems are multi-channel in nature, but multiple channels might include noisy and redundant information and increase computational times of automated EEG decoding algorithms. To reduce the signal-to-noise ratio, improve accuracy and reduce computational time, one may combine channel […]
- 08/2021 - Radiotherapy & Oncology
- 04/2021 - Annals of Surgical Oncology
- 03/2021 - Biometrical Journal
In clinical practice, it is often the case where the association between the occurrence of events and time-to-event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account […]