A Bayesian approach for estimating brain functional connectivity networks in resting-state fMRI data (in preparation), joint work with Alice Chevaux (phd student at UGA, France), Julyan Arbel (Associate researcher at Inria Grenoble, France), Guillaume Kon Kam King (Senior researcher at Inrae, France), Wendy Meiring (Professor at UC Santa Barbara, US), Alex Petersen (Associate professor at BYU, US), Sophie Achard (Senior researcher CNRS, France).
Y. Jiang, J. Bigot. Wasserstein multivariate autoregressive models and its application in graph learning from distributional time series. arXiv preprint arXiv: 2207.05442. 2022, submitted to Journal of Time Series Analysis.
Y. Jiang, J. Bigot & S. Maabout. Online graph topology learning from matrix-valued time series. Computational Statistics & Data Analysis, 2025, 202, 108065.
Y. Jiang, G. Vergara-Hermosilla. Extended SIRU model with dynamic transmission rate and its application in the forecasting of COVID-19 under temporally varying public intervention. Biomath 13 (2024), 2412176.
Y. Jiang, J. Bigot & S. Maabout. Sensor selection on graphs via data-driven node sub-sampling in network time series. arXiv preprint arXiv:2004.11815. 2020.
Y. Jiang, J. Bigot, E. Provenzi. Commutativity of spatiochromatic covariance matrices in natural image statistics. Mathematics in Engineering. 2020, 2(2): 313-339. The work is conducted in the context of the Master 2 internship at Institut de Mathématiques de Bordeaux.
Statistical analysis of spatio-temporal and multi-dimensional data from a network of sensors. (Defense slides)
Reviewer for Biometrika, Biometrics.
F. Coppini, Y. Jiang, S. Tabti. Predictive models on 1D signals in a small-data environment. Hal: hal-03211100. 2020. This report is the result of work during the Semaine d’'Etudes Math'ematiques et Entreprises, where 4 teams of PhD students were dedicated to solve the problems proposed by the partner companies.