Michela Carlotta Massi
PhD Student in Data Analytics and Decision Sciences


MOX Laboratory for Modeling and Scientific Computing

Department of Mathematics, Politecnico di Milano

Center for Health Data Science (CHDS)

Human Technopole

My research focuses on developing effective methodologies to represent highly complex genomic and medical data,

to enhance and complement interpretable and robust statistical approaches to classification, regression and survival modelling

to personalize treatment decisions within the precision medicine framework.


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Massi, M. C., Ieva, F., Gasperoni, F., & Paganoni, A. M. (2021).


Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble

Statistical Analysis and Data Mining: the ASA Data Science Journal

Franco N.R., Massi M.C., Ieva F. et al. (2021)

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

Radiotherapy and Oncology

Massi, M.C., Gasperoni, F., Ieva, F., Paganoni, A.m., Zunino, P., Manzoni, A., Franco, N.r., Et Al. (2020)


A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort


Frontiers in Oncology


Massi, M.C., Ieva, F. (2021)


Learning Signal Representations for EEG Cross-subject Channel Selection and Trial Classification 


IEEE International Workshop on Machine Learning for Signal Processing


Manduchi, L., Marcinkeviks, R., Massi, M.C. et al. (2022)


A Deep Variational Approach to Clustering Survival Data

10th International Conference on Learning Representations, ICLR 2022



Massi, M. C., Dominoni L., Ieva, F., Fiorito G. (2022) 


A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: an application to Breast Cancer time to diagnosis


Massi, M. C., Franco, N.R., Ieva, F. et al (2021) 


Learning High-Order Interactions via Targeted Pattern Search