Scuola superiore sant'anna

Professore Ordinario

Francesca Chiaromonte


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, and 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. 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 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.”


Below are my main peer-reviewed publications sorted by area [LAST UPDATED Feb 2021]:

Methodology in Statistics and Bioinformatics

  1. Nandy D. Chiaromonte F., Li R. (2021). Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems. Journal of the American Statistical Association. /01621459.2020.1864380.
  2. 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. 1845184.
  3. 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.
  4. Cremona M.A., Xu H., Makova K.M., Reimherr M., Chiaromonte F., Madrigal P. (2019) Functional data analysis for computational biology. Bioinformatics.
  5. Yao W., Nandy D., Lindsay B.G., Chiaromonte F. (2018). Covariate Information Matrix for Sufficient Dimension Reduction. Journal of the American Statistical Association.
  6. 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.
  7. 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 in CRAN.
  8. 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.
  9. Liu Y., Chiaromonte F., Ross H., Malhotra R., Elleder D. and Poss M. (2015) Error correction and statistical analyses for intra-host comparisons of feline immunodeficiency virus diversity from high-throughput sequencing data. BMC Bioinformatics. 16(202). DOI: 10.1186/s12859-015-0607-z.
  10. Goldstein J., Haran M., Simeonov I., Fricks J. and Chiaromonte F. (2015). An attraction-repulsion point process model for respiratory syncytial virus infections. Biometrics. 71(2), 376–385 (student paper competition winner, Graybill/ENVR 2014 conference).
  11. Chiaromonte F. and Makova K.D. (2014). Using statistics to shed light on the dynamics of the human genome: a review. Advances in Complex Data Modeling and Computational Methods to Statistics, Contributions in Statistics. A. Paganoni and P. Secchi (eds), Springer Intl Publishing, SW. 69-85.
  12. Kuruppumullage Don P., Lindsay B. and Chiaromonte F. (2014). Model-based block clustering with EM algorithm (reviewed; 2014 student paper award finalist, ASA Nonparametric Statistics Section).
  13. Lee K.Y., Li B. and Chiaromonte F. (2013) A general theory of nonlinear sufficient dimension reduction: formulation and estimation. Annals of Statistics. 41(1), 221-249. doi:10.1214/12-AOS1071
  14. Chiaromonte F. and Taylor J. (2010) Information Based Agglomerative Segmentation in Metric Spaces. Journal of the Indian Society of Agricultural Statistics, 64(1), 33-44.
  15. Cook R.D., Li B. and Chiaromonte F. (2010) Envelope models for parsimonious and efficient multivariate linear regression. Discussion paper. Statistica Sinica, 20(3), 927-910 (including comments and rejoinder).
  16. Kosakovsky Pond S., Wadhawan S., Chiaromonte F., Ananda G., Chung W.Y., Taylor J., Nekrutenko A. and The Galaxy Team. (2009) Windshield splatter analysis with the Galaxy metagenomic pipeline. Genome Research, 19, 2144-2153.
  17. Tyekucheva S. and Chiaromonte F. (2008) Augmenting the bootstrap to analyze high dimensional genomic data. Invited discussion article Test, 17, 1-18 (article), 47-55 (rejoinder).
  18. Cook R.D., Li B. and Chiaromonte F. (2007) Dimension reduction in regression without matrix inversion. Biometrika, 94, 569-584.
  19. Taylor J., Tyekucheva S., King D.C., Hardison R., Miller W. and Chiaromonte F. (2006) ESPERR: Learning strong and weak signals in genomic sequence alignments to identify functional elements. Genome Research, 16, 1596-1604.
  20. Li B., Zha H. and Chiaromonte F. (2005) Contour regression: a general approach to dimension reduction. Annals of Statistics, 33(4), 1580-1616.    
  21. Kolbe D., Taylor J., Elnitski L., Eswara P., Li J., Miller W., Hardison R.C. and Chiaromonte F. (2004) Regulatory potential scores from genome-wide 3-way alignments of human, mouse and rat. Genome Research, 14, 700-707.
  22. Li B., Cook R.D. and Chiaromonte F. (2004) Dimension reduction for the conditional mean in regressions with categorical predictors. Annals of Statistics, 30, 1636-1668.
  23. Li B., Zha H. and Chiaromonte F. (2004) Linear contour learning: a method for supervised dimension reduction. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. ACM International Conference Proceeding Series. 349-356.
  24. Chiaromonte F., Bing Yap V. and Miller W. (2002) Scoring pairwise genomic sequence alignments. Proceedings of the Pacific Symposium on Biocomputing 2002.
  25. Chiaromonte F., Martinelli J.A. (2002) Dimension reduction strategies for analyzing global gene expression data with a response. Mathematical Biosciences, 176 (1),123-144.
  26. Chiaromonte F., Cook R.D. and Li B. (2002) Sufficient dimension reduction in regressions with categorical predictors. Annals of Statistics, 30(2). 475-497
  27. Chiaromonte F. and Cook R.D. (2002) Sufficient dimension-reduction and graphics in regression. Annals of the Institute of Statistical Mathematics, 54(4) 768-795.
  28. Chiaromonte F. (2001). Graphics and sufficient dimension reduction with continuous and categorical predictors. Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, C. Provasi (ed), Cleup, Padova ITALY. 39-44.
  29. Chiaromonte F. (1998). On multivariate structures and exhaustive reductions. Computing Science and Statistics, 30, S. Weisberg (ed), Interface Foundation of North America, Fairfax Station VA, 204-213.
  30. Chiaromonte F. (1997). A reduction paradigm for multivariate laws. L1 Statistical Procedures and Related Topics, Y. Dodge (ed), Institute of Mathematical Statistics Monograph Series, Hayward CA, 229-240.

Applications in “Omics” and Biomedical Sciences

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Pangenomics Consortium (2016). Computational pan-genomics: status, promises and challenges. Briefings in Bioinformatics 19(1), 118-135.
  9. 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: 10.1371/journal.pcbi.1004956
  10. 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: 10.1073/pnas.1409328111
  11. Campos-Sanchez R., Kapusta A., Feschotte C., Chiaromonte F. and Makova K.D. (2014). Genomic landscape of human, bat and ex vivo DNA transposon integration. Molecular Biology and Evolution, 31(7), 1816–1832 doi:10.1093/molbev/msu138
  12. Kuruppumullage Don P., Andanda G., Chiaromonte F. and Makova K.D. (2013) Segmenting the human genome based on states of neutral genetic divergence. Proceedings of the National Academy of Sciences USA, 110(36), 14699–14704. doi:10.1073/pnas.1221792110
  13. Ananda G., Walsh E., Jacob K.D., Krasilnikova M., Eckert K.A., Chiaromonte F., Makova K.D. (2012) Distinct mutational behaviors distinguish simple tandem repeats from microsatellites in the human genome. Genome Biology and Evolution, 5(3), 606–620. doi: 10.1093/gbe/evs116
  14. Wagstaff B.J., Hedges D.J., Derbes R.S., Campos Sanchez R., Chiaromonte F., Makova K.D. and Roy-Engel A.M. (2012) Rescuing Alu: recovery of new inserts shows LINE-1 preserves Alu activity through A-tail expansion. PLoS Genetics, 8(8) e1002842.
  15. Fungtammasan A., Walsh E., Chiaromonte F., Eckert K.A., Makova K.D. (2012) A Genome-Wide Analysis of Common Fragile Sites: What Features determine chromosomal instability in the human genome? Genome Research, 22, 993-1005.
  16. Kelkar Y.D., Eckert K.A. Chiaromonte F. and Makova K.D. (2011) A matter of life and death: how microsatellites emerge in and vanish from the human genome. Genome Research, 21(12), 2038-2048. PMID:21994250
  17. Wu W., Cheng Y., Keller C.A., Kumar S.A., Ernst J., Mishra T., Morrissey C., Dorman C.M., Chen K.B., Drautz D., Giardine B., Shibata Y., Song L., Crawford G.E., Furey T.S., Kellis M., Miller W., Taylor J., Schuster S.C., Zhang Y., Chiaromonte F., Blobel G.L., Weiss M.J. and Hardison R.C. (2011) Dynamics of the Epigenetic Landscape During Erythroid Differentiation after GATA1 Restoration. Genome Research, 21(10), 1659-1671. PMID:21795386
  18. Ananda G., Chiaromonte F. and Makova K.D. (2011) A genome-wide view of mutation rate co-variation using multivariate analyses. Genome Biology, 12(3):R27. doi:10.1186/gb-2011-12-3-r27. PMID:21426544
  19. Simeonov I., Gong X., Kim O., Poss M., Chiaromonte F. and Fricks J. (2010) Exploratory spatial analysis of in vitro Respiratory Syncytial Virus co-infections. Viruses, 2(12), 2782-2802; doi:10.3390/v2122782
  20. Kelkar Y.D., Strubczewski N., Hile S.E., Chiaromonte F., Eckert K.A. and Makova K.D. (2010) What Is a Microsatellite: A Computational and Experimental Definition Based upon Repeat Mutational Behavior at A/T and GT/AC Repeats. Genome Biology and Evolution, 2, 620-635. doi: 10.1093/gbe/evq046
  21. Schuster S., Miller W. et al. (2010) Complete Khoisan and Bantu genomes from southern Africa. Nature, 463, 943-947.
  22. Cheng Y., Wu W., Kumar S.A., Yu D., Deng W., Tripic T., King D.C., Chen K.B.,  Zhang Y., Drautz D., Giardine B., Schuster S.C., Miller W., Chiaromonte F., Zhang Yu, Blobel G.A., Weiss M.J. and Hardison R.C. (2009) Erythroid GATA1 function revealed by genome-wide analysis of transcription factor occupancy, histone modifications and mRNA expression. Genome Research, 19, 2172-2184.
  23. Roy S., Lavine J., Chiaromonte F., Terwee J., VandeWoude S., Bjornstad O. and Poss M. (2009) Multivariate statistical analyses demonstrate unique host immune responses to single and dual lentiviral infection. PLoS ONE, 4(10) e7359. doi:10.1371/journal.pone.0007359
  24. Zhang Y., Wu W., Cheng Y., King D.C., Harris R.S., Taylor J., Chiaromonte F. and Hardison R.C. (2009) Primary sequence and epigenetic determinants of in vivo occupancy of genomic DNA by GATA1. Nucleic Acids Research. doi: 10.1093/nar/gkp747
  25. Kvikstad E.M., Chiaromonte F. and Makova K.D. (2009) Ride the wavelet: a multi-scale analysis of genomic context flanking small insertions and deletions. Genome Research, 19, 1153-1164.
  26. Cheng Y., King D.C., Dore L.C., Zhang X., Zhou Y., Zhang Y., Dorman C., Abebe D., Kumar S., Chiaromonte F., Miller W., Green R.D., Weiss M.J. and Hardison R.C. (2008) Transcriptional enhancement by GATA1-occupied DNA segments is strongly associated with evolutionary constraint on the binding site motif. Genome Research, 18, 1896-1905.
  27. Kelkar Y., Tyekucheva S., Chiaromonte F. and Makova K. (2008) The genome-wide determinants of microsatellite evolution. Genome Research, 18, 30-38.
  28. Tyekucheva S., Makova K., Karro J. Hardison R.C., Miller W. and Chiaromonte F. (2008) Human-macaque comparisons illuminate variation in neutral substitution rates. Genome Biology, 9(4): R76. Highly accessed article.
  29. Gutiérrez R.A., Lejay L., Chiaromonte F., Shasha D.E. and Coruzzi G.M. (2007) Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis. Genome Biology, 8(1): R7. 
  30. King D.C., Taylor J., Zhang Y., Cheng Y., Lawson H.A., Martin J., ENCODE groups for Transcriptional Regulation and Multispecies Alignment, Chiaromonte F., Miller W. and Hardison R.C. (2007) Finding cis-regulatory modules using comparative genomics: some lessons from ENCODE data. Genome Research, 17, 775-786.
  31. Kvikstad E.M., Tyekucheva S., Chiaromonte F. and Makova K.D. (2007) A macaque’s-eye view of human insertions and deletions: differences in mechanisms. PLoS Computational Biology, 3(9) e176, 1772-1782.
  32. Cesari P., Chiaromonte F. and Newell K.M. (2007) Support Vector Machines Categorize the Scaling of Human Grip Configurations. Behavior Research Methods, 39(4), 1001-1007.
  33. Wang H., Zhang Y., Petrykowska H., Cheng Y., Zhou Y., King D., Kasturi J., Taylor J., Chiaromonte F., Miller W., Welch J., Weiss M. and Hardison R. (2006) Experimental validation of predicted mammalian erythroid cis-regulatory modules. Genome Research, 16, 1480-1492.
  34. Carrel L., Park C., Tyekucheva S., Dunn J., Chiaromonte F. and Makova K.D. (2006) Genomic environment predicts expression patterns on the human inactive X chromosome. PLoS Genetics, 2(9) e151, 1477-1486.
  35. Taylor J., Tyekucheva S., Zody M., Chiaromonte F. and Makova K. (2006) Strong and weak male mutation bias at different sites in the primate genomes: insights from the human-chimpanzee comparison. Molecular Biology and Evolution, 23(3), 565-573.
  36. King D.C., Taylor J., Elnitski L., Chiaromonte F., Miller W. and Hardison R.C. (2005) Evaluation of regulatory potential and conservation scores for detecting cis-regulatory modules in aligned mammalian genome sequences. Genome Research, 15, 1051-1060.
  37. Gibbs R. et al., Rat Genome Sequencing Project Consortium. (2004) Genome sequence of the brown Norway rat yields insights into mammalian evolution. Nature,428,493-521.
  38. Hillier L. et al. International Chicken Genome Sequencing Consortium (2004). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432, 695–716.
  39. Makova K.D., Yang S. and Chiaromonte F. (2004) Insertions and deletions are male-biased too: a whole-genome analysis in rodents. Genome Research, 14, 567-573.
  40. Yang S., Smit A.F., Schwartz S., Chiaromonte F., Roskin K. M., Haussler D., Miller W. and Hardison R.C. (2004) Patterns of insertions and their covariation with substitutions in the rat, mouse and human genomes. Genome Research, 14, 517-527.
  41. Hardison R.C., Chiaromonte F., Kolbe D., Wang H., Petrykowska H., Elnitski L., Yang S., Giardine B., Zhang Y., Riemer C., Schwartz S., Haussler D., Roskin K., Weber R., Diekhans M., Kent W.J., Weiss M.J., Welch J. and Miller W. (2003) Global prediction and tests for erythroid regulatory regions. Cold Spring Harbor Symposia in Quantitative Biology: The Genome of Homo Sapiens, 68, 335-345.
  42. Chiaromonte F., Weber R. J., Roskin K.M., Diekhans M., Kent W.J. and Haussler D. (2003) The share of human genomic DNA under selection estimated from human-mouse genomic alignments. Cold Spring Harbor Symposia in Quantitative Biology: The Genome of Homo Sapiens, 68, 245-255.
  43. Elnitski L., Hardison R., Li J., Yang S., Kolbe D., Eswara P., O’Connor M., Schwartz S., Miller W. and Chiaromonte F. (2003) Distinguishing regulatory DNA from neutral sites. Genome Research, 13, 64-72.
  44. Hardison R., Roskin K.M., Yang S., Diekhans M., Kent J.W., Weber R., Elnitski L., Li J., O’Connor M., Kolbe D., Schwartz S., Furey T.S., Whelan S., Goldman N., Smit A., Miller W., Chiaromonte F. and Haussler D. (2003) Co-variation in frequencies of substitution, deletion, transposition and recombination during eutherian evolution. Genome Research, 13, 13-26.
  45. Chiaromonte F., Miller W. and Bouhassira E. (2003) Gene length and proximity to neighbors affect genome-wide expression levels. Genome Research, 13, 2602-2608.
  46. Waterston, R. et al., International Mouse Genome Sequencing consortium (2002) Initial sequencing and comparative analysis of the mouse genome. Nature. 420, 520-562.
  47. Chiaromonte F., Yang S., Elnitski L., Bing Yap V., Miller W. and Hardison R. (2001). Association between divergence and interspersed repeats in mammalian noncoding genomic DNA. Proceedings of the National Academy of Sciences USA, 98(25), 14503-14508.

Meteorology, Climate, Economics and Social Sciences applications

  1. 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.
  2. Tripodi G., Chiaromonte F. and Lillo F. (2020) Knowledge and social relatedness shape research portfolio diversification. Scientific Reports 10, 14232.
  3. 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.
  4. 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.
  5. 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
  6. Veren D., Evans J.L., Jones S. and Chiaromonte F. (2009) Novel Metrics for Evaluation of Ensemble Forecasts of Tropical Cyclone Structure. Monthly Weather Review, 137(9), 2830–2850.
  7. Evans J.L., Arnott J. and Chiaromonte F. (2006) Evaluation of operational model cyclone structure forecasts during extratropical transition. Monthly Weather Review, 134, 3054-3072.
  8. Arnott J., Evans J.L. and Chiaromonte F. (2004) Characterization of extratropical transition using cluster analysis. Monthly Weather Review, 132(12), 2916–2937.
  9. Chiaromonte F. and Dosi G. (1993). Heterogeneity, competition, and macro-economic dynamics. Structural Change and Economic Dynamics, 4(1), 39-63.
  10. Chiaromonte F. and Dosi G. (1993). The microfoundations of competitiveness and their macro-economic implications. Technology and the Wealth of Nations; The Dynamics of Constructed Advantage, C. Freeman, D. Foray (eds). Pinter, London UK, New York NY, 107-134.
  11. Chiaromonte F., Dosi G., Orsenigo L. (1993). Innovative learning and institutions in the process of developments: on the microfoundations of growth regimes. Learning and Technological Change, R. Thompson (ed). MacMillan, London UK, 117-149.

Contributo su Rivista

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.”


Below is a selection of representative invited lectures I gave at international conferences and universities (out of ~80) [LAST UPDATED AUG 2019]:

  1. “Cook's Distance and Beyond” Conference. Minneapolis, MN. 03/2019. Information Matrices and Group Structures in Sufficient Dimension Reduction.
  2. 4th ISNPS Conference (special invited speaker). Salerno, ITALY. 06/2018. Information Matrices and Group Structures in Sufficient Dimension Reduction.
  3. 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.
  4. EcoSta 2017. Hong Kong, CHINA. 06/2017. Structured Sufficient Dimension Reduction and its Applications.
  5. IWSM 2016 Conference (plenary speaker). Rennes, FRANCE. 07/2016. Functional Data Analysis at the boundary of “Omics”.
  6. 3rd ISNPS Conference. Avignon, FRANCE. 06/2016. Structured Sufficient Dimension Reduction and its applications.
  7. ISNPS Meeting 2015. Graz, AUSTRIA. 07/2015. Exploiting structure to reduce and integrate high-dimensional, under-sampled “Omics” data.
  8. 7th ERCIM Conference. Pisa, ITALY. 12/2014. Exploiting structure to reduce and integrate high dimensional, under sampled “Omics” data.
  9. 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.
  10. Yale University, Department of Biostatistics. New Haven CT, USA. 11/2013. Segmenting the human genome based on states of neutral genetic divergence.
  11. 1st ISNPS Conference. Chalkidiki, GREECE. 06/2012. Statistical characterizations of genome dynamics.
  12. University of Minnesota, 40th Anniversary Reunion of the School of Statistics. Minneapolis MN, USA. 05/2011. A Statistician’s travels in Omics-land.
  13. 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.
  14. ITA 2009 Conference, UCSD. San Diego, CA. 02/2009. The words to predict it: finding patterns in high-dimensional comparative genomics spaces.
  15. UC Berkeley, Department of Statistics. Berkeley CA. 02/2009. Neyman Seminar. Strategies to analyze high-dimensional and under-sampled genomics data.


Istituto di Economia

  • Consiglio Istituto di Economia