Research

Overview

My work is situated at the interface of applied mathematics, computational statistics, and data science. I develop interpretable methods for analysing complex data, with particular attention to reproducibility, open-source software, and translation into usable tools.

Research areas

Densities and anomalies

Density estimation

Development of GLBFP methods, for Generalized Linear Blend Frequency Polygons, extending ideas from histograms, average shifted histograms, and linear blend frequency polygons. The goal is to produce stable, adaptive, and geometrically interpretable estimators, especially for atypical-observation detection.

GLBFP

Directional data

Circular statistics

Models for circular, axial, and directional data, including angular responses, circular random effects, and visualizations adapted to angles, orientations, directions, and times of day.

CircularRegression ggcircular

Latent dynamics

Hidden Markov models

Modeling of temporal and spatio-temporal data with hidden-state models, with particular interest in the effect of temporal scale on inference, multi-individual models, and links with step-selection functions.

Thesis and code

Translation

Applied data science

Development of resources and tools connecting statistical methods, machine learning, interpretability, robustness, and data science education.

Teaching resources

Doctoral thesis

Title: General Multi-state Models for the Analysis of Animal Movement

Year: 2024

University: Université Laval

Supervisors: Thierry Duchesne and Louis-Paul Rivest

This thesis develops a general framework for multi-state modeling of animal movement. It combines the rigor of hidden Markov models with the flexibility of habitat-selection functions, through a unifying formulation connecting behavioral and spatial approaches.

View thesis Code and reproducible material

Software and dissemination

  • GLBFP: density estimation based on Generalized Linear Blend Frequency Polygons.
  • CircularRegression: R package available on CRAN for fitting regression models when the response is circular.
  • ggcircular: ggplot2 extension for circular, axial, and directional data.
  • DonutMap: R package for creating donut maps with sf, ggplot2, and leaflet.
  • site_ressources_SSD: francophone educational resources platform for data science.
  • GeneralOaxaca: R package for generalized Blinder-Oaxaca decomposition.

Recent presentations

  • SSC 2026. A Leave-One-Out Influence Statistic for Scalable Density-Based Outlier Detection, with Thierry Duchesne and Michel Carbon. Presentation PDF and GitHub repository. This presentation received the 2026 New Investigator Presentation Award.

Publications

Published articles

  1. Nicosia, A. (2026). Beyond the next step: A multi-criteria generative validation framework for step selection functions. Methods in Ecology and Evolution, 17(6), 1754-1767. https://doi.org/10.1111/2041-210X.70313

  2. Nicosia, A. (2026). Discussion of “Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies”. Journal of Data Science. https://doi.org/10.6339/26-JDS1208H

  3. Gagnon, S., Allard, M., Nicosia, A. (2018). Diurnal and seasonal variations of tundra CO₂ emissions in a polygonal peatland near Salluit, Nunavik, Canada. Arctic Science, 4(1), 1-15. https://doi.org/10.1139/AS-2016-0045

  4. Nicosia, A., Duchesne, T., Rivest, L.-P., Fortin, D. (2017). A Multi-State Conditional Logistic Regression Model for the Analysis of Animal Movement. Annals of Applied Statistics, 11(3), 1537-1560. https://doi.org/10.1214/17-AOAS1045

  5. Nicosia, A., Duchesne, T., Rivest, L.-P., Fortin, D. (2017). A General Hidden State Random Walk Model for Animal Movement. Computational Statistics & Data Analysis, 105, 76-95. https://doi.org/10.1016/j.csda.2016.07.009

  6. Rivest, L.-P., Duchesne, T., Nicosia, A., Fortin, D. (2016). A General Angular Regression Model for the Analysis of Data on Animal Movement in Ecology. Journal of the Royal Statistical Society: Series C, 65(3), 445-463. https://doi.org/10.1111/rssc.12124

Preprints

  1. Nicosia, A. (2026). Sequential predictive e-diagnostics for hidden Markov models of animal movement. bioRxiv. https://doi.org/10.64898/2026.07.07.737005

  2. Bouderbala, I., Nicosia, A., Fortin, D. (2026). Behavioural state inference from movement and environmental data using Markovian step selection functions. bioRxiv. https://doi.org/10.64898/2026.02.05.704063