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Bio

I am a statistician developing methods for the analysis of large, high-dimensional and complex data, and applying such methods in several scientific fields – including contemporary “Omics” sciences, Meteorology and Economics.

I received a Laurea (cum laude) in Statistic and Economic Sciences from the University of Rome La Sapienza (Rome, IT), where I worked with Giovanni Dosi on a thesis titled Processes of Microeconomic Innovation and Macroeconomic Dynamics, and a Ph.D. in Statistics from the University of Minnesota (Minneapolis, MN, USA), where I worked with R. Dennis Cook on a thesis titled A Reduction Paradigm for Multivariate Laws.

At the Sant’Anna School of Advanced Studies I am a faculty in the Institute of Economics, the scientific coordinator of EMbeDS (a MIUR-funded Department of Excellence for Economics and Management in the era of Data Science) since 2018, the internal referent for the PhD in Data Science (established as a consortium with the Scuola Normale Superiore, the University of Pisa, the CNR and the IMT of Lucca in 2017) and for the PhD in AI for Society (one of the five graduate programs constituting the National Doctorate in Artificial Intelligence established in 2021). At Penn State (University Park, PA, USA) I work in the Department of Statistics, I have a courtesy affiliation with the Department of Public Health Sciences, and I am active in the Institute for Genome Sciences (one of the Huck Institutes of the Life Sciences), the Center for Computational Biology and Bioinformatics and the Center for Medical Genomics. In 2019, I have been named the Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for the Life Sciences.

Other academic institutions where I entertain collaborations and spent time over the years include the MOX laboratory of the Politecnico di Milano (Milan, IT), the Istituto di Analisi dei Sistemi e Informatica of the CNR (Rome, IT), the Institute for Pure and Applied Mathematics of UCLA (Los Angeles, CA, USA), the Courant Institute of Mathematical Sciences and the Department of Biology of NYU (New York, NY, USA), the International Institute for Applied Systems Analysis (Laxenburg, AT), and the Santa Fe Institute (Santa Fe NM, USA).

Since 2016, I am a Fellow of the American Statistical Associationfor outstanding collaborative work in high throughput biology, contributions to methodology in statistics and bioinformatics, commitment to interdisciplinary research, and leadership in developing training programs at the interface of statistics, computation and the life sciences.”

Since 2022, I am a Fellow of the Institute of Mathematical Statisticsfor outstanding contributions to methodology for the analysis of large, complex and structured data, in particular to the fields of sufficient dimension reduction and envelope models, for outstanding interdisciplinary work in the ‘Omics’ and biomedical sciences, and for leadership in interdisciplinary training and mentoring efforts.”

[LAST UPDATED Aug 2022]

Pubblicazioni

Ricerca

My interests as a statistician include methods to analyze high-dimensional, complex and potentially under-sampled regression and classification problems (in particular dimension reduction and feature selection methods); computational techniques for the empirical assessment of significance (e.g., re-sampling, perturbation and permutation schemes); latent structure and Markov modeling approaches; and functional data analysis methods.

Most of my applied research occurs at the interface between Statistics and contemporary “Omics” sciences. This work comprises interdisciplinary collaborations with biologists and computer scientists in which large genomic, epigenomic, transcriptomic, metagenomic (microbiomes) and metabolomic data sets are analyzed to investigate various aspects of genome dynamics, evolution and function – and to characterize human diseases.

In other interdisciplinary collaborations, I work on Meteorology applications where clustering and re-sampling techniques are used to improve forecast and delineate structure and lifecycle of tropical storms; on statistical analyses of the socioeconomic impacts of climate change; and on statistical methods for inference, validation and emulation of agent-based models in Economics.

Over the years, my research has been supported by several awards from U.S. funding agencies such as the NIH (National Institutes of Health) and the NSF (National Science Foundation).

Since 2016, I am a Fellow of the American Statistical Association “for outstanding collaborative work in high throughput biology, contributions to methodology in statistics and bioinformatics, commitment to interdisciplinary research, and leadership in developing training programs at the interface of statistics, computation and the life sciences.”

Since 2022, I am a Fellow of the Institute of Mathematical Statistics “for outstanding contributions to methodology for the analysis of large, complex and structured data, in particular to the fields of sufficient dimension reduction and envelope models, for outstanding interdisciplinary work in the ‘Omics’ and biomedical sciences, and for leadership in interdisciplinary training and mentoring efforts.”

[LAST UPDATED Aug 2022]

Corsi

Below is a selection of representative invited lectures I gave at conferences and universities (out of ~95) [LAST UPDATED Aug 2022]:

  1. Universita' Cattolica, Milano, ITALY. 05/2022. Information matrices and numbers in large supervised problems.
  2. Neyman Seminar, Department of Statistics, UC Berkeley. Berkeley CA, USA. 10/2021. COVID-19 in Italy: characterizing epidemic waves through functional data analysis.
  3. CTSI-BERD Seminar, Penn State University. University Park, PA USA. 04/2021. How functional data analysis contributes to biomedical research: the genetics of childhood obesity, and the unfolding of COVID-19 in Italy.
  4. 14th ERCIM Conference. London, UK. 12/2021. Functional data analysis characterizes the shapes of the COVID-19 epidemic in Italy.
  5. Penn State University, Department of Public Health Sciences, Hershey PA, USA. 11/2019. "Omics" perspectives on childhood obesity.
  6. “Cook's Distance and Beyond” Conference. Minneapolis, MN. 03/2019. Information Matrices and Group Structures in Sufficient Dimension Reduction.
  7. 4th ISNPS Conference (special invited speaker). Salerno, ITALY. 06/2018. Information Matrices and Group Structures in Sufficient Dimension Reduction.
  8. SIAM Workshop on Dimensionality Reduction (plenary speaker). Pittsburgh, PA. 07/2017. Sufficient Dimension Reduction for large regression problems: basic notions and approaches to leverage known structure across predictors and observations.
  9. EcoSta 2017. Hong Kong, CHINA. 06/2017. Structured Sufficient Dimension Reduction and its Applications.
  10. IWSM 2016 Conference (plenary speaker). Rennes, FRANCE. 07/2016. Functional Data Analysis at the boundary of “Omics”.
  11. 3rd ISNPS Conference. Avignon, FRANCE. 06/2016. Structured Sufficient Dimension Reduction and its applications.
  12. ISNPS Meeting 2015. Graz, AUSTRIA. 07/2015. Exploiting structure to reduce and integrate high-dimensional, under-sampled “Omics” data.
  13. 7th ERCIM Conference. Pisa, ITALY. 12/2014. Exploiting structure to reduce and integrate high dimensional, under sampled “Omics” data.
  14. SCO 2013 Conference, Politecnico di Milano. Milan, ITALY. 09/2013. Common fragile sites, microsatellites and genome dynamics: old and new statistics for human genomic data.
  15. Yale University, Department of Biostatistics. New Haven CT, USA. 11/2013. Segmenting the human genome based on states of neutral genetic divergence.
  16. 1st ISNPS Conference. Chalkidiki, GREECE. 06/2012. Statistical characterizations of genome dynamics.
  17. University of Minnesota, 40th Anniversary Reunion of the School of Statistics. Minneapolis MN, USA. 05/2011. A Statistician’s travels in Omics-land.
  18. IPAM Program on Mathematical and Computational Approaches in High-Throughput Genomics, UCLA. Los Angeles, CA. 10/2011. Genome-wide statistical analyses of mutagenic processes and their interactions.
  19. ITA 2009 Conference, UCSD. San Diego, CA. 02/2009. The words to predict it: finding patterns in high-dimensional comparative genomics spaces.
  20. Neyman Seminar, Department of Statistics, UC Berkeley. Berkeley CA. 02/2009. Strategies to analyze high-dimensional and under-sampled genomics data.