Francesca Chiaromonte

My interests as a statistician include methods to analyze high-dimensional, complex, structured and potentially under-sampled data. These include dimension reduction and feature selection methods for regression and classification problems; computational techniques for the empirical assessment of significance and stability (e.g., re-sampling, perturbation, permutation and augmentation 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 worked 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 large datasets involved in gauging 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. (e.g., National Institutes of Health, National Science Foundation) and Italian funding agencies. I published over 120 peer reviewed articles in generalist venues (e.g., Nature, Proceedings of the National Academy of Sciences USA, Nature Scientific Reports) as well as statistical, computational, and domain-specific venues (e.g., Annals of Statistics, the Journal of the American Statistical Association, Biometrika, Biometrics, Statistica Sinica, the Journal of Computational and Graphical Statistics, Statistics & Computing, NeurIPS, Bioinformatics, PLoS Computational Biology, PLoS Biology, PLoS Genetics, Genome Research, Genome Biology, Molecular Biology & Evolution, Nucleic Acids Research, the Journal of Developmental Origins of Health and Disease, Pediatric Obesity, the Journal of Environmental Economics and Management, Environmental Research Letters, the Journal of Economic Dynamics and Control, Applied Network Science, the Monthly Weather Review).

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.”

In 2026 I have been elected Fellow of the American Association for the Advancement of Science (class of 2025) “For distinguished contributions to the field of statistics, the development of methods for the analysis of large, complex and structured data, and their applications in the biomedical and social sciences.”

[LAST UPDATED June 2026]

Below is a selection of representative invited lectures I gave at conferences and universities (out of over 100) [LAST UPDATED June 2026]:

• UVA–IMT Workshop on Artificial Intelligence, its applications and global impact, IMT. Lucca, ITALY.  05/2026. Who’s got the steering wheel? Thoughts from a runaway car.
• Symposium on Statistical Learning for Complex Data, Universite Laval. Quebec CA. 11/2025. Tackling supervised problems when the data is (ultra) high-dimensional or structured: an overview (keynote).
• IBS-IR-EMR 2025 Conference. Salerno, ITALY. 09/2025. Feature Screening and Feature Selection: approaches for high and ultra-high dimensional supervised problems in biomedical research.
• 2024 EHEW (Empirical Health Economics Workshop). Pisa, ITALY. 07/2024. Functional Data: leveraging the information in shapes (keynote).
• NESCI3 Workshop (Network for Statistical and Causal Inference), IMT. Lucca, ITALY. 07/2024. COVID-19 in Italy: characterizing pre-vaccine epidemic waves through Functional Data Analysis.
• Statistics Seminar, Auburn University. Auburn AL, USA10/2023. Functional Data and its use in Biomedical Applications: Power and Perils.
• Statistics Public Lecture. Penn State University, University Park PA, USA. 09/2023. What can shapes teach us? leveraging functional data in biomedical applications.
• WWWGLS Seminar, Penn State University. University Park PA, USA. 11/2022. Reducing, selecting and leveraging structure in contemporary data – some reflections, looking ahead.
• Geisser Distinguished Lecture, School of Statistics, University of Minnesota. Minneapolis MN, USA. 10/2022. In Awe of Today's Data: Reducing, Selecting, Leveraging Structures – and Looking Ahead.Universita' Cattolica, Milano, ITALY. 05/2022. Information matrices and numbers in large supervised problems.
• Neyman Seminar, Department of Statistics, UC Berkeley. Berkeley CA, USA. 10/2021. COVID-19 in Italy: characterizing epidemic waves through functional data analysis.
• 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.
• 14^th ERCIM Conference. London, UK. 12/2021. Functional data analysis characterizes the shapes of the COVID-19 epidemic in Italy.
• Penn State University, Department of Public Health Sciences. Hershey PA, USA. 11/2019. "Omics" perspectives on childhood obesity.
• “Cook's Distance and Beyond” Conference. Minneapolis MN, USA. 03/2019. Information Matrices and Group Structures in Sufficient Dimension Reduction.
• 4^th ISNPS Conference. Salerno, ITALY. 06/2018. Information Matrices and Group Structures in Sufficient Dimension Reduction (special invited speaker).
• SIAM Workshop on Dimensionality Reduction. Pittsburgh PA, USA. 07/2017. Sufficient Dimension Reduction for large regression problems: basic notions and approaches to leverage known structure across predictors and observations (plenary speaker).
• EcoSta 2017. Hong Kong, CHINA. 06/2017. Structured Sufficient Dimension Reduction and its Applications.
• IWSM 2016 Conference. Rennes, FRANCE. 07/2016. Functional Data Analysis at the boundary of “Omics” (plenary speaker).
• 3^rd ISNPS Conference. Avignon, FRANCE. 06/2016. Structured Sufficient Dimension Reduction and its applications.
• ISNPS Meeting 2015. Graz, AUSTRIA. 07/2015. Exploiting structure to reduce and integrate high-dimensional, under-sampled “Omics” data.
• 7^th ERCIM Conference. Pisa, ITALY. 12/2014. Exploiting structure to reduce and integrate high dimensional, under sampled “Omics” data.
• 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.
• Yale University, Department of Biostatistics. New Haven CT, USA. 11/2013. Segmenting the human genome based on states of neutral genetic divergence.
• 1^st ISNPS Conference. Chalkidiki, GREECE. 06/2012. Statistical characterizations of genome dynamics.
• University of Minnesota, 40^th Anniversary Reunion of the School of Statistics. Minneapolis MN, USA. 05/2011. A Statistician’s travels in Omics-land.
• IPAM Program on Mathematical and Computational Approaches in High-Throughput Genomics, UCLA. Los Angeles CA, USA. 10/2011. Genome-wide statistical analyses of mutagenic processes and their interactions.
• ITA 2009 Conference, UCSD. San Diego CA, USA. 02/2009. The words to predict it: finding patterns in high-dimensional comparative genomics spaces.
• Neyman Seminar, Department of Statistics, UC Berkeley. Berkeley CA, USA. 02/2009. Strategies to analyze high-dimensional and under-sampled genomics data.