PhD student Connie Zhang has received a 2025 National Science Foundation Graduate Research Fellowship for her work on developing methods for high-dimensional multivariate time series analysis through GARCH models. The model, which she is developing in partnership with faculty member Ali Shojaie, shows promise in identifying biomarkers for Alzheimer's disease, which could facilitate earlier diagnosis and more effective interventions.
"The NSF Fellowship will allow Connie to focus on developing novel statistical machine learning methods to infer dynamic brain functional connectivity networks and develop noninvasive biomarkers for detecting patients at early stages of Alzheimer’s Disease, when recently developed treatments can be most effective,” said Shojaie.
Zhang is also working with Eardi Lila to develop new methods for sparse Canonical Correlation Analysis (CCA) to improve consistency when analyzing high-dimensional data. And she works as a research assistant with Lyndia Brumback, conducting statistical analyses on longitudinal data across multiple healthcare projects, including ICU patient outcomes and readmissions related to acute respiratory failure.
Zhang will receive three years of financial support through the Fellowship Program, which is designed to help ensure the quality, vitality, and strength of the scientific and engineering workforce of the United States.
