Francesca Chiaromonte

Professor of Statistics, Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for the Life Sciences
Francesca Chiaromonte
Francesca
Chiaromonte
Professor of Statistics, Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for the Life Sciences
Interests
High-dimensional & Undersampled data
Dimension Reduction & Feature Selection Methods
Regression & Multivariate Methods
Bioinformatics
"Omics" & Biomedical applications
Meteorology, Climate, Economics applications
Address
Phone
(814) 865-7075
Website
Penn State Personal Site

Biography

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, 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. 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. She served as director of the Institute for Genome Sciences (one of the Huck Institutes of the Life Sciences) from 2010 to 2025. In addition to the graduate program in Statistics, she belongs to the faculty of the interdisciplinary Bioinformatics & Genomics graduate program.

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 L’EMbeDS (Economics, Management and Law in the era of Data Science). She contributed to the prior PhD in Data Science (established as a consortium with the Scuola Normale Superiore, the University of Pisa, and other partners) and currently contributes to the PhD in AI for Society (one of five graduate programs constituting the Italian National Doctorate in Artificial Intelligence).

Francesca is a Fellow of the American Statistical Association since 2016, “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.” She is also a Fellow of the Institute for Mathematical Statistics since 2022, "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 she was 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.”

 

Selected recent Penn State news

Four Penn State faculty members elected AAAS Fellows (2026)

Statistics department to host free public lecture Sept. 21 - Penn State (2025)

Beyond the double helix: Alternative DNA conformations in ape genomes. (2025)

Honey bee colony loss in the U.S. linked to mites, extreme weather, pesticides. (2023)

Are egg cells in aging primates protected from mutations? (2022)

Chiaromonte and Hunter Named 2022 IMS Fellows (2022)

New approach can help identify young children most at risk for obesity (2022)

Staying home, primary care, and limiting contagion hubs may curb COVID-19 deaths (2021)

A new class of functional elements in the human genome? (2021)

Unusual DNA folding increases the rates of mutations (2021)

Scientists take a step toward understanding 'jumping genes' effect on the genome (2020)

Connecting the dots: using curves to analyze multifaceted data may hold the key to understanding childhood obesity (2020)

Costs of natural disasters are increasing at the high end (2019)

Chiaromonte named first Huck Chair in Statistics for the Life Sciences (2019)

 

Selected recent publications

STATISTICS AND BIOINFORMATICS METHODS

  • Testa L., Boschi T., Chiaromonte F., Kennedy E.H., Reimherr M. (2026) Doubly-Robust Functional Average Treatment Effect Estimation. Journal of Causal Inference. 14(1). doi.org/10.1515/jci-2025-0045. 

  • Tonini S., Vandin A., Chiaromonte F., Licari D. and Barsacchi F. (2026) Fast, robust, and accurate anomaly detection for multivariate time series. Advances in Data Analysis and Classification. doi.org/10.1007/s11634-026-00667-8

  • Dang H., Cremona M.A. and Chiaromonte F. (2025) smoothEM: a new approach for the simultaneous assessment of smooth patterns and spikes. Electronic Journal of Statistics. 19 (2). doi.org/10.1214/25-EJS2428.

  • Di Iorio J., Cremona M.A. and Chiaromonte F. (2025). Amplitude-Invariant Functional Motif Discovery. In Aneiros, Bongiorno, Goia, Hušková (eds) New Trends in Functional Statistics and Related Fields. IWFOS 2025. Contributions to Statistics. Springer, Cham. doi.org/10.1007/978-3-031-92383-8_21

  • Di Iorio J., Cremona M.A. and Chiaromonte F. (2025) funBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores. Statistics & Computing. 35, 11. doi.org/10.1007/s11222-024-10537-y

  • Boschi T., Testa L., Chiaromonte F., Reimherr M. (2024) FAStEN: an efficient adaptive method for feature selection and estimation in high-dimensional functional regressions. Journal of Computational and Graphical Statistics. 34(2). doi.org/10.1080/10618600.2024. 2407464. 

  • Casaluce R., Burattin A., Chiaromonte F., Lafuente A.L., Vandin A. (2024) White-box validation of quantitative product lines by statistical model checking and process mining. Journal of Systems & Software. 210, 111983. doi.org/10.1016/j.jss.2024.111983

  • Cremona M.A. and Chiaromonte F. (2023) Probabilistic K-means with local alignment for functional motif discovery. Journal of Computational and Graphical Statistics. 32(3), 1119-1130. doi.org/10.1080/10618600.2022.2156522.

  • Casaluce R., Burattin A., Chiaromonte F., Vandin, A. (2023). Process Mining Meets Statistical Model Checking: Towards a Novel Approach to Model Validation and Enhancement. In Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops BPM 2022. Lecture Notes in Business Information Processing, v. 460 243–256. Springer, Cham. doi.org/10.1007/978-3-031-25383-6_18.

  • Insolia L., Kenney A., Chiaromonte F., Felici G. (2022) Simultaneous Feature Selection and Outlier Detection with Optimality Guarantees. Biometrics. 78(4), 1592-1603. doi.org/10.1111/biom.13553. 

  • Nandy D. Chiaromonte F., Li R. (2022). Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems. Journal of the American Statistical Association. 117(539), 1516-1529. doi.org/10.1080 /01621459.2020.1864380.

  • Pisztora V., Ou Y., Huang X., Chiaromonte F., Li J. (2022). Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff. STAT. 11(1) e425. doi.org/10.1002/sta4.425.

  • Boschi T., Reimherr M.L., Chiaromonte F. (2021) A highly efficient Group Elastic Net algorithm with applications to Function-on-Scalar regression. NeurIPS 2021.
  • Insolia L., Kenney A., Calovi M., Chiaromonte F. (2021) Robust Variable Selection with Optimality Guarantees for High-dimensional Logistic Regression. Stats (special issue on Robust Statistics in Action) 4(3), 665-681. doi.org/10.3390/stats4030040.
  • Insolia L., Kenney A., Chiaromonte F., Felici G. (2021) Simultaneous Feature Selection and Outlier Detection with Optimality Guarantees. Biometrics. doi.org/10.1111/biom.13553.
  • Boschi T., Chiaromonte F., Li B., Secchi P. (2021) Covariance based low-dimensional registration for function-on-function regression. STAT. 10(1) e404. doi.org/10.1002/sta4.404.
  • Insolia L., Chiaromonte F., Riani M. (2021) A Robust Estimation Approach for Mean-Shift and Variance-Inflation Outliers. Festschrift in Honor of R. Dennis Cook. E. Bura and B. Li (eds), Springer, Cham. doi.org/10.1007/978-3-030-69009-0_2.
  • 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

  • Zhang X., Mohanty S., Chiaromonte F., Makova K.D. (2026) Substitution spectrum and selection at G-quadruplexes in great ape telomere-to-telomere genomes. Genome Biology and Evolution. 18(3). doi.org/10.1093/gbe/evag040.
  • Torres-González E., Arbeithuber B., Stoler N., Cremona M.A., Shebl O., Ebner T., Tiemann-Boege I., Diaz F., Chiaromonte F., Makova K.D. (2026). Mammalian Mitochondrial DNA Accumulates Insertions and Deletions with Age in Energetically Demanding Tissues. Molecular Biology and Evolution. msag035. doi.org/10.1093/molbev/msag035.
  • Boschi T., Di Iorio J., Testa L., Cremona M.A., Chiaromonte F. (2025) Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis. Scientific Reports. doi.org/10.1038/s41598-025-29316-4.

  • Arbeithuber B., Anthony K., Higgins B., Oppelt P., Shebl O., Tiemann-Boege I., Chiaromonte F., Ebner T., Makova K.D. (2025) Allele frequency selection and no age-related increase in human oocyte mitochondrial mutations. Science Advances. 11, eadw4954. doi.org/10.1126/sciadv.adw4954. 

  • Mohanty S.K., Chiaromonte F., Makova K.D. (2025) Evolutionary dynamics of predicted G-quadruplexes in human and other great apes. Genome Biology. 26, 161. doi.org/10.1186/s13059-025-03635-1. 

  • Smeds L., Kamali K., Kejnovská I., Kejnovský E., Chiaromonte F., Makova K.D. (2025) Non-canonical DNA in human and other ape telomere-to-telomere genomes. Nucleic Acids Research. 53, 7, gkaf298. doi.org/10.1093/nar/gkaf298.   

  • Carmisciano L., Boschi T., Chiaromonte F., Delmastro F., Vandin A. (2024) Investigating Functional Data Analysis for Wearable Physiological Sensor Data in Stress Evaluation. IEEE Symposium on Computers and Communications (ISCC). doi.org/10.1109/ISCC61673.2024. 10733576 (with this paper, L. Carmisciano received a Best Paper award at the 4th IEEE International Conference on ICT Solutions for eHealth, 2024, Paris FR)

  • Larivier D., Craig S., Paul I., Hohoman E., Savage J., Wright R., Chiaromonte F., Makova K., Reimherr M. (2024) Methylation profiles at birth linked to early childhood obesity. Journal of Developmental Origins of Health and Disease. 15: e7. doi.org/10.1017/ S2040174424000060. 

  • Weissensteiner M.H., Cremona M.A., Guiblet W., Stoler N., Harris R.S., Cechova M., Chiaromonte F., Huang Y. and Makova K.D. (2023) Accurate sequencing of DNA motifs able to form alternative (non-B) structures. Genome Research. 33 907-922. doi.org/ 10.1101/gr.277490.122.

  • Insolia L., Molinari R., Rogers S.R., Williams G.R., Chiaromonte F. and Calovi M. (2022) Honeybee colony loss linked to parasites, pesticides and extreme weather across the United States. Scientific Reports. 12,20787. doi.org/10.1038/s41598-022-24946-4. 

  • Onoja A., Picchiotti N., Fallerini C., Baldassarri M., Fava F., GEN-COVID Multicenter Study, Colombo F., Chiaromonte F., Renieri A., Furini S., Raimondi F. (2022) An explainable model of host genetic interactions linked to COVID-19 severity. Communications Biology. 5, 1133. doi.org/10.1038/s42003-022-04073-6.

  • Arbeithuber B., Cremona M., Hester J., Barrett A., Higgins B., Anthony K., Chiaromonte F., Diaz F.J., Makova K.D. (2022). Advanced age increases frequencies of de novo mitochondrial mutations in macaque oocytes and somatic tissues. Proceedings of the National Academy of Sciences USA, 119 (15) e2118740119. doi.org/10.1073/pnas.2118740119.
  • Nandy D., Craig S.J.C., Cai J., Tian Y., Paul I.M., Savage J.S., Marini M.E., Hohman E.E., Reimherr M.L., Patterson A.D., Makova K.D., Chiaromonte F. (2021) Metabolomic profiling of stool of two-year old children from the INSIGHT study reveals links between butyrate and child weight outcomes. Pediatric Obesity, 17(1) e12833. doi.org/10.1111/ijpo.12833.
  • Craig S., Kenney A.M., Lin J., Paul I.M., Birch L.L., Savage J.S., Marini M.E., Chiaromonte F., Reimherr M.L. and Makova K.D. (2021) Constructing a polygenic risk score for childhood obesity using functional data analysis. Econometrics and Statistics. doi.org/10.1016/ j.ecosta.2021.10.014.
  • Boschi T., Di Iorio J., Testa L., Cremona M., Chiaromonte F. (2021) Functional Data Analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy. Scientific Reports. 11, 17054. doi.org/10.1038/s41598-021-95866-y.
  • 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

  • Coronese M., Crippa F., Lamperti F., Chiaromonte F., Roventini A. (2025) Raided by the storm: how three decades of thunderstorms shaped US incomes and wages. Journal of Environmental Economics and Management. 130, 103074. doi.org/10.1016/j.jeem.2024.103074. 

  • Tripodi G., Lillo F., Mavilia R., Mina A., Chiaromonte F., Lamperti F. (2024) The public use of early-stage scientific advances in carbon dioxide removal: a science-technology-policy-media perspective. Environmental Research Letters. 19 (11), 114009. doi.org/10.1088/1748-9326/ad7479.

  • Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G. (2022). Venture capital investments through the lens of network and functional data analysis. Applied Network Science. 7, 42 (2022). doi.org/10.1007/s41109-022-00482-y.
  • Vandin A., Giachini D., Lamperti F., Chiaromonte F. (2022) Automated and distributed statistical analysis of economic agent-based models. Journal of Economic Dynamics and Control. doi.org/10.1016/j.jedc.2022.104458.
  • 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. 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