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Marzia Angela
Cremona
Adjunct Assistant Professor
Marzia Angela

Biography

Marzia received a B.Sc. (2009) and M.Sc. (2011) in Mathematics from Università degli Studi di Milano. In 2016 she obtained a Ph.D. in Mathematical Models and Methods in Engineering from Politecnico di Milano, with a thesis titled, Statistical methods for omics data (Advisor: Piercesare Secchi; Co-advisors: Laura M. Sangalli and Simone Vantini).

Currently, Marzia is an Assistant Professor in the Department of Operations and Decision Systems at Université Laval. In 2016 she joined the Department of Statistics at the Pennsylvania State University, working as Postdoctoral Researcher with Francesca Chiaromonte and Kateryna D. Makova, and from 2017 as Bruce Lindsay Visiting Assistant Professor. Marzia is also an affiliate faculty at the CHU de Québec – Université Laval research center (Population health and optimal health practices axis).

She is also a member of the Center for Medical Genomics of the Pennsylvania State University, and of the Big Data Research Center of Université Laval, and of CIRRELT (Interuniversity Research Centre on Enterprise, Networks, Logistics and Transportation).

 

Publications

  • Di Iorio, Chiaromonte, Cremona (2020) On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics btaa060.
     
  • Mei, Arbeithuber, Cremona, DeGiorgio, Nekrutenko (2019) A high resolution view of adaptive event dynamics in a plasmid. Genome Biology and Evolution 11(10): 3022–3034.
     
  • Cremona, Xu, Makova, Reimherr, Chiaromonte, Madrigal (2019) Functional data analysis for computational biology. Bioinformatics 35(17): 2311–2313.
     
  • Guiblet*, Cremona*, Cechova, Harris, Kejnovska, Kejnovsky, Eckert, Chiaromonte, Makova (2018) Long-read sequencing technology indicates genome-wide effects of non-B DNA on polymerization speed and error rate. Genome Research, 28: 1767-1778. Press release
     
  • Cremona*, Pini*, Cumbo, Makova, Chiaromonte, Vantini (2018) IWTomics: testing high-resolution sequence-based “Omics” data at multiple locations and scales. Bioinformatics 34(13): 2289–2291.
     
  • Campos-Sànchez*, Cremona*, Pini, Chiaromonte, Makova (2016) Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. PLoS Computational Biology 12(6): e1004956.
     
  • Cremona, Liu, Hu, Bruni, Lewis (2016) Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging. Reliability Engineering and System Safety 154: 49-59.
     
  • Cremona, Sangalli, Vantini, Dellino, Pelicci, Secchi, Riva (2015) Peak shape clustering reveals biological insights. BMC Bioinformatics 16:349.