We are delighted to announce the next of our NeST Online Seminar Series.

Thursday Jan 15th 2026, 4.30pm (London time)

Title: Dynamic Networks Modeling for High-Dimensional Non-Stationary Time Series
Speaker: Professor Ali Shojaie (University of Washington)

Abstract: 

High-dimensional time series from changing environments are collected in many applications and are prevalent in biology and medicine. Ignoring these changes, or assuming stationarity, results in erroneous conclusions. Moreover, as we demonstrate in neuroscience applications, non-stationary time series can reveal changes in brain connectivity in disease conditions or in response to external stimuli. Motivated by these applications, we present two approaches for inferring changes in brain connectivity networks from non-stationary high-dimensional time series. The first approach directly infers the changes in brain connectivity networks in the spectral domain. We develop efficient estimation and inference procedures for the change in the brain network connectivity and illustrate its utility in detecting changes in brain connectivity resulting from optogenetics stimulation. The second approach considers a high-dimensional Markov switching vector autoregressive (VAR) model, a VAR model whose transition matrices depend on the states of an (unobserved) discrete Markov process. We propose an approximate Expectation-Maximization (EM) algorithm to estimate the model parameters and establish the consistency of the resulting estimates.

For further details, please contact one of the online seminar organisers, Andy Jones (Andy.J.Jones@ed.ac.uk) or Cristian Jimenez Varon (cristian.jimenezvaron@york.ac.uk) for further information and/or the link to the seminar.