Gianna S. Monti

Gianna S. Monti

Associate Professor of Statistics at University of Milano-Bicocca

About Me

The most interesting scientific questions usually live exactly where the standard methods stop working—and that’s where I choose to work.

I am an Associate Professor of Statistics (STAT-01/A) at the University of Milano-Bicocca, within the Department of Economics, Management, and Statistics (DEMS).

My work is driven by the conviction that complex data—whether biological or environmental—require statistical frameworks tailored to their actual structure, not forced into standard assumptions. I specialize in developing methodologies that handle the constraints of modern high-dimensional data without sacrificing mathematical rigor.

Research Interests & Expertise

My academic journey is anchored in the development of robust and nonparametric inference, with a primary focus on:

  • Compositional Data Analysis (CoDa): Exploring the geometry of data representing parts of a whole, ensuring that the relative nature of information is preserved.
  • High-Dimensional Microbiome Data: Developing sparse regression models aimed at controlling the False Discovery Rate (FDR) in complex genomic settings.
  • Ecological Risk Assessment: Tailoring statistical methods to quantify environmental impact and species sensitivity, bridging the gap between theoretical statistics and sustainability.
  • Methodological Toolkit: Extensive experience in Mixture Models and Bayesian Hierarchical Modeling to account for complex data structures and uncertainty.

Scientific Community & Leadership

I am deeply committed to the advancement of the international statistical community. I have been elected Vice President of the CODA Association for the 2025–2029 term.

I also contribute my expertise to several interdisciplinary research centers, fostering collaboration across diverse scientific domains:

Additionally, I serve as an Associate Editor for Statistical Methods and Applications and am an active member of the Italian Statistical Association (SIS) and the International Association for Statistical Computing (IASC-ISI).