Francesca Chiaromonte is a Professor of Statistics and, since 2019, the Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for the Life Sciences at Penn State.
She received a Laurea (cum laude) in Statistic and Economic Sciences from the University of Rome La Sapienza (Rome, IT), and a Ph.D. in Statistics from the University of Minnesota.
Her research 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. She applies these methods in contemporary “Omics” sciences and other scientific fields – including Meteorology and Economics.
At Penn State, Francesca holds a courtesy affiliation with the Department of Public Health Sciences. She is a member of the Center for Computational Biology and Bioinformatics and of the Center for Medical Genomics, and the director of the Institute for Genome Sciences (one of the Huck Institutes of the Life Sciences).
She also works in the Institute of Economics of the Sant’Anna School of Advanced Studies (Pisa, IT), where she is the scientific coordinator for Economics and Management in the era of Data Science (EMbeDS), and she contributes to a PhD in Data Science — established as a consortium with the Scuola Normale Superiore, the University of Pisa, and other partners in the area.
Since 2016, Francesca is 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.”
Selected recent publications
STATISTICS AND BIOINFORMATICS METHODS
- Nandy D. Chiaromonte F., Li R. (2021). Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems. Journal of the American Statistical Association. doi.org/10.1080 /01621459.2020.1864380.
- Kenney A., Chiaromonte F. and Felici G. (2020) MIP-BOOST: Efficient and Effective L0 Feature Selection for Linear Regression. Journal of Computational and Graphical Statistics. doi.org/10.1080/10618600.2020. 1845184.
- Di Iorio J., Chiaromonte F., Cremona M.A. (2020) On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics 36(9) 2955–2957. doi.org/10.1093/bioinformatics/btaa060.
- Cremona M.A., Xu H., Makova K.M., Reimherr M., Chiaromonte F., Madrigal P. (2019) Functional data analysis for computational biology. Bioinformatics. doi.org/10.1093/bioinformatics/btz045.
- Yao W., Nandy D., Lindsay B.G., Chiaromonte F. (2018). Covariate Information Matrix for Sufficient Dimension Reduction. Journal of the American Statistical Association. doi.org/10.1080/01621459.2018.1515080.
- Cremona M.A., Pini A., Cumbo F., Makova K.D., Chiaromonte F. and Vantini S. (2018) IWTomics: testing high-resolution sequence-based “Omics” data at multiple locations and scales. Bioinformatics. doi.org/10.1093/bioinformatics/bty090.
- Liu Y., Chiaromonte F. and Li B. (2017) Structured Ordinary Least Squares: a sufficient dimension reduction approach for regressions with partitioned predictors and heterogeneous units. Biometrics. doi:10.1111/biom.12579.
- Bartolucci F., Chiaromonte F., Kuruppumullage Don P. and Lindsay B.G. (2016) Composite likelihood inference in a discrete latent variable model for two-way “clustering-by-segmentation” problems. Journal of Computational and Graphical Statistics doi:10.1080/10618600.2016.1172018
"OMICS" AND BIOMEDICAL APPLICATIONS
- Guiblet W., Cremona M.A., Harris R.S., Chen D., Eckert K.A., Chiaromonte F., Huang Y., Makova K.D. (2021) Non-B DNA: A major contributor to small- and large-scale variation in nucleotide substitution frequencies across the genome. Nucleic Acids Research gkaa1269. doi.org/10.1093/nar/gkaa1269.
- Chen D., Cremona M.A., Qi Z., Mitra R., Chiaromonte F., Makova K.D. (2020) Human L1 Transposition Dynamics Unraveled with Functional Data Analysis. Molecular Biology and Evolution 37(12), 3576–3600. doi.org/10.1093/molbev/msaa194.
- Mughal M., Koch H., Huang J., Chiaromonte F., De Giorgio M. (2020) Learning the properties of adaptive regions with functional data analysis. PLoS Genetics 16(8): e1008896. doi.org/10.1371/journal.pgen.1008896.
- Arbeithuber B., Hester J., Cremona M.A., Stoler N., Zaidi A., Higgins B., Anthony K., Chiaromonte F., Diaz F.J., Makova K.D. (2020) Age-related accumulation of de novo mitochondrial mutations in mammalian oocytes and somatic tissues. PLoS Biology. 18(7): e3000745. doi.org/10.1371/journal.pbio.3000745.
- Cechova M., Harris R.S., Tomaszkiewicz M., Arbeithuber B., Chiaromonte F., Makova K.D. (2019) High satellite repeat turnover in great apes studied with short- and long-read technologies. Molecular Biology and Evolution. 36(11), 2415–2431. doi.org/10.1093/molbev/msz156.
- Guiblet W.M., Cremona M.A., Cechova M., Harris R.S., Kejnovská I., Kejnovsky E., Eckert K., Chiaromonte F., Makova K.D.(2018). Long-read sequencing technology indicates genome-wide effects of non-B DNA on polymerization speed and error rate. Genome Research 28(12), 1767-1778. doi.org/10.1101/gr.241257.118.
- Craig S., Blankenberg D., Parodi A., Paul I.M., Birch L.L., Savage J.S., Marini M.E., Stokes J.L., Nekrutenko A., Reimherr M., Chiaromonte F., Makova K.D. (2018). Child weight gain trajectories linked to oral microbiota composition. Scientific Reports 8(1), 14030. doi.org/10.1038/s41598-018-31866-9.
- Pangenomics Consortium (2018). Computational pan-genomics: status, promises and challenges. Briefings in Bioinformatics 19(1), 118-135. doi.org/10.1093/bib/bbw089.
- Campos-Sanchez R., Cremona M., Pini A., Chiaromonte F. and Makova K.D. (2016) Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with Functional Data Analysis. PLoS Computational Biology 12(6): e1004956. doi.org/10.1371/ journal.pcbi.1004956.
- Rebolledo-Jaramilloa B., Shu-Wei M., Stoler N., McElhoec J.A., Dickins B., Blankenberg D., Korneliussen T.S., Chiaromonte F., Nielsen R., Holland M.M., Paul I., Nekrutenko A. and Makova K.D. (2014). Maternal age effect and severe germ-line bottleneck in the inheritance of human mitochondrial DNA. Proceedings of the National Academy of Sciences USA 111(43), 15474–15479. doi.org/10.1073/pnas.1409328111.
METEOROLOGY, CLIMATE, ECONOMICS AND SOCIAL SCIENCES APPLICATIONS
- Nanni, M., Andrienko, G., Barabási, A. et al. (2021). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Ethics and Information Technology. doi.org/10.1007/s10676-020-09572-w
- Tripodi G., Chiaromonte F. and Lillo F. (2020) Knowledge and social relatedness shape research portfolio diversification. Scientific Reports 10, 14232. doi.org/10.1038/s41598-020-71009-7.
- Coronese M., Lamperti F., Keller K., Chiaromonte F. and Roventini A. (2020) Reply to Geiger and Stomper: On capital intensity and observed increases in the economic damages of extreme natural disasters. Proceedings of the National Academy of Sciences USA. 117 (12) 6314-6315. doi.org/10.1073/pnas.1922722117.
- Coronese M., Lamperti F., Keller K., Chiaromonte F. and Roventini A. (2019) Evidence of sharp increase in the economic damages of natural disasters (2019). Proceedings of the National Academy of Sciences USA .
- Kuruppumullage Don P., Evans J.L., Chiaromonte F. and Kowaleski A.M. (2016) Mixture-Based Path Clustering for Synthesis of ECMWF Ensemble Forecasts of Tropical Cyclone Evolution. Monthly Weather Review. doi:10.1175/MWR-D-15-0214.1