Marzia A. Cremona is an assistant professor in data science in the department of Operations and Decision Systems at Laval University, and a researcher at the CHU de Québec – Université Laval research center. She obtained a Ph.D. in Mathematical Models and Methods for Engineering from Politecnico di Milano (Italy), and she joined Université Laval after spending four years at the Pennsylvania State University (US). Her research interests focus on the development of statistical and computational methods for the analysis of large, high-dimensional and complex data, and the application of such methods in computational biology. Indeed, much of her work is at the interface between statistics and “Omics” sciences.

Functional data analysis in computational biology

“Omics” data generated by Next Generation Sequencing (NGS) techniques pose several challenges for reliable statistical analysis that are needed to unveil biological mechanisms and the consequences they have on genome function and evolution, as well as on diseases. Most of these data are suitable to be considered at high resolution and represented as curves over the genome. Functional Data Analysis (FDA), a subfield of statistics that aim at analyzing curves (mathematical functions), plays a critical role in exploiting the output of NGS assays. Indeed, considering curves as statistical units endowed with shapes increases our ability to extract both interpretable global patterns and relevant local information from these data, allowing sophisticated biological interpretation of shape information.

An important part of Dr. Cremona’s research program concerns the development of novel FDA techniques, with the aim of broaden the scope of FDA to many areas of computational biology. An important aspect of her research is its collaborative and multidisciplinary nature. Indeed, she is involved in multiple international collaborations involving the analysis of various types of “Omics” data, for example on molecular evolution, evolutionary genomics and human genetics.

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