Statistician, currently Assistant Professor (RTDB) at the Department of Economics, Management, and Quantitative Methods of University of Milan. My methodological interests are related to the domain of applied statistics and statistical learning, with particular focus on mixture modeling. I am a passionate R user and a Tidyverse fan.
Honored to have received the Young Researcher Paper Award at Cladag 2023 conference! I presented a working paper on clustering lifetime data with frailties and random covariates, a collaboration with colleagues Ieva, F., Masci, C. and Paganoni, A.M. from the MOX Health Analytics research area
New Preprint! Sparse model-based clustering of three-way data via lasso-type penalties (joint work with Casa, A. and Fop, M.)
PhD in Statistics and Mathematical Finance, 2020
University of Milano-Bicocca
MSc in Statistical Sciences (with honors), 2015
University of Padua
BSc in Statistics and Management (with honors), 2012
University of Padua
University
Assistant Professor, Department of Economics, Management, and Quantitative Methods (2024/02-Ongoing)
University of Milan
Assistant Professor, Department of Mathematics (2021/04-2024/02)
Politecnico di Milano
Teaching Assistant, BSc courses in Statistics and Statistical Methods (2019/02-2021/07)
University of Milano-Bicocca
Postdoctoral research fellow, Department of Statistics and Quantitative Methods (2020/04-2021/03)
University of Milano-Bicocca
Teaching Assistant, BSc course in Statistics (2017/09-2018/02)
Bocconi University
Industry
Freelance data scientist (2020/01-2020/04)
DCG, Milan
Business analyst and planner (2015/09-2016/09)
HP Inc, Barcelona
Visiting periods
Visiting PhD Student (2018/03-2019/03)
Insight Centre for Data Analytics, University College Dublin
Exchange Semester (2014/01-2014/06)
School of Economics and Management, Tilburg University
Articles in refereed journals
Benetti, L., Boniardi, E., Chiani, L., Ghirri, J., Mastropietro, M., Cappozzo, A., & Denti, F. (2023)
Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets
Advances in Data Analysis and Classification (online first)
link |
arXiv |
code
Cappozzo, A., Ieva, F., Fiorito, G. (2023)
A general framework for penalized mixed-effects multitask learning with applications on DNA methylation surrogate biomarkers creation
Annals of Applied Statistics 17(4), 3257-3282
link |
arXiv |
code
Cappozzo, A., García Escudero, L.A., Greselin, F., Mayo-Iscar, A. (2023)
Graphical and computational tools to guide parameter choice for the cluster weighted robust model
Journal of Computational and Graphical Statistics 32(3),1195–1214
link |
code
Casa, A., Cappozzo, A., Fop, M. (2022)
Group-wise shrinkage estimation in penalized model-based clustering
Journal of Classification, 39(3), 648–674.
link |
arXiv |
code
Cappozzo, A., McCrory, C., Robinson, O. et al. (2022)
A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events
Clinical Epigenetics 14, 121
link |
code
Cappozzo, A., García Escudero, L.A., Greselin, F., Mayo-Iscar, A. (2021)
Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling
Stats 2021, 4(3)
link |
code
Denti, F., Cappozzo, A., Greselin, F. (2021)
A Two-Stage Bayesian Nonparametric Model for Novelty Detection with Robust Prior Information
Statistics and Computing, 32, 18
link |
arXiv |
code
Cappozzo, A., Duponchel, L., Greselin, F., Murphy, T.B. (2021)
Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food
Analytica Chimica Acta, 1153, 338245
link |
arXiv |
code |
cover
Cappozzo, A., Greselin, F., Murphy, T.B. (2021)
Robust variable selection for model-based learning
in presence of adulteration
Computational Statistics & Data Analysis, 158, 107186
link |
arXiv |
code
Cappozzo, A., Greselin, F., Murphy, T.B. (2020)
Anomaly and Novelty detection for robust semi-supervised learning.
Statistics and Computing, 30, 1545–1571
link|
arXiv |
code
Cappozzo, A., Greselin, F., Murphy, T.B. (2020)
A robust approach to model-based classification based on trimming and constraints.
Advances in Data Analysis and Classification, 14(2), 327-354
link |
arXiv |
code
Submitted and working papers
Cappozzo, A., Casa, A., Fop, M. (2023+)
Sparse model-based clustering of three-way data via lasso-type penalties
Submitted
arXiv |
code
Rossi, A., Cappozzo, A., & Ieva, F. (2023+)
Functional Boxplot Inflation Factor adjustment through Robust Covariance Estimators
Submitted
mox report
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code
Young Researcher Paper Award (2023/09)
CLADAG 2023 14-th Scientific Meeting Classification and Data Analysis Group
Salerno, Italy
Best poster presentation (2018/09)
MBC2 Workshop on Models and Learning for Clustering and Classification
Catania, Italy
Member of the third best team in terms of algorithm predictive accuracy (2017/09)
Young CLADAG - Data science competition
Politecnico di Milano, Italy
Member of one of the four winning teams (2017/06)
Stats Under the Stars 3 - Data science competition
Università degli Studi di Firenze, Italy
Successful participant to HP Business Academy Assessment Week (2015/07)
Hewlett Packard Española and Fundación Universidad-Empresa
Barcelona, Spain
Member of the Italian Statistical Society, its young group y-SIS and the Institute of Mathematical Statistics
Volounteer data scientist at HeartInData, a group of data professionals based in Milan (IT) offering their passion, dedication and talents for social good.
Leaf at the Mathematics Genealogy Project. Are you a scholar in math or a related field? You should consider adding your dissertation, and let the branches grow!