MICHELA CARLOTTA MASSI
Michela Carlotta Massi, PhD
Postdoc Researcher
AFFILIATIONS
PREVIOUS AFFILIATIONS
Center for Health Data Science (CHDS)
Human Technopole
Health Data Science Centre, Di Angelantonio Group
PhD in Data Analytics and Decision Sciences, Politecnico di Milano​
MOX Lab for Modeling and Scientific Computing
Dept. of Mathematics, Politecnico di Milano
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., 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
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PLOS Computational Biology
Massi, M. C., Ieva, F., Gasperoni, F., & Paganoni, A. M. (2021).
Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble
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Statistical Analysis and Data Mining: the ASA Data Science Journal
Franco N.R., Massi M.C., Ieva F. et al. (2021)
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Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity
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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
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10th International Conference on Learning Representations, ICLR 2022
RELEVANT PREPRINTS
Massi, M. C., Franco, N.R. et al (2022)
Learning High-Order Interactions for Polygenic Risk Scoring