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Francesca
Chiaromonte
Professor of Statistics
Francesca Chiaromonte

Biography

Francesca Chiaromonte is a Professor of Statistics and 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.

At Penn State, Chiaromonte 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). In 2019, she was named the Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for 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 novel, PhD in Data Science — established as a consortium with the Scuola Normale Superiore, the University of Pisa, and other partners in the area.

 

Publications

METEOROLOGY, CLIMATE, AND ECONOMICS APPLICATIONS

  • 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 116 (43) 21450-21455. doi.org/10.1073/pnas.1907826116
  • 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
  • Veren D., Evans J.L., Jones S. and Chiaromonte F. (2009) Novel Metrics for Evaluation of Ensemble Forecasts of Tropical Cyclone Structure. Monthly Weather Review137(9), 2830–2850.

 

OMICS AND BIOMEDICAL APPLICATIONS

  • 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. To appear. (BioRxiv doi.org/10.1101/470054)
  • 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 Research28(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

 

STATISTICS AND BIOINFORMATICS METHODS

  • 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. R-package deposited on CRAN.