Director of the **Florence Center for Data Science** (FDS) and coordinator of the Statistics track for the **PhD program in Mathematics, Computer Science, Statistics**

## Research

My primary research interests revolve around the fascinating world of multivariate statistical models,
with a particular focus on graphical
models. A graphical model is a multivariate statistical model
that can be associated to a graph: nodes correspond to random variables
and missing edges to conditional independences. I am also
interested in marked point processes for event history analysis,
multilevel and mixture models in non-standard situations, models for circular data, and in their
connection with graphical models. I recently got involved in
statistical learning, data science and models for high-dimensional
data. As I love to be challenged by new topics, I have absolutely no
idea on what I'm going to do in my future.

## Some publications

Bravi R., Cohen E.J., Martinelli, Gottard A. &
Minciacchi D. (2018) The Less You are, the More You are Helped:
Effect of Kinesio Tapeon Temporal Coordination, Int J Sports Med, doi:
10.1055/a-0668-0041.

Kateri M., Gottard A. & Tarantola C. (2017) Generalized Quasi
Symmetry Models for Ordinal Contingency Tables, Australian & New Zealand
Journal of Statistics, 59(3), 239-253.

Gottard A. & Calzolari G. (2017) Alternative estimating procedures for multiple membership logit models with mixed effects: indirect inference and data cloning, Journal of Statistical Computation and Simulation, 87(12), 2334-2348.

recently submitted (more or less)

Gottard A. & Panzera A. Graphical models for circular variables

recent presentations

Anna Gottard (2023). Uncertainty & Fairness metrics. In: Book of Short Papers SIS 2023

## Teaching

MASL: Multivariate Analysis & Statistical Learning - Moodle link

FSL: Fundation of Statistical Learning - Moodle link