MICHELA CARLOTTA MASSI

Michela Carlotta Massi
PhD Student in Data Analytics and Decision Sciences
AFFILIATIONS
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.
RECENT ARTICLES
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
RELEVANT PREPRINTS
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