- High-dimensional statistics
- Time series analysis
- Machine learning and signal processing
Research supported as the Principal Investigator (PI) by the National Science Foundation grant (NSF DMS-1404891, 2014-2018), Research Board Awards (RB17092, 2017-2018; RB15004, 2014-2017) and a start-up grant from UIUC.
I am currently interested in the analysis of high-dimensional multiple time series data. Statistical estimation and inference for high-dimensional time-dependent data are notoriously difficult and challenging since (i) the temporal dependence is nonlinear; (ii) the underlying data structures are quite complicated in high-dimensions; (iii) the observations may have heavier tails than subgaussian distributions; and (iv) the data generation mechanism may exhibit certain dynamic features. My research work focuses on a broad spectrum of the second-order estimation and inference problems for high-dimension time series including estimation of the space-time covariance matrix, time-varying graphical models, and their related functionals and latent structures.
On the application side, my theoretical and methodological work provides the guidance for a wide range of modern applications in the spatiotemporal Big Data analytics such as the construction of brain connectivity networks using functional Magnetic Resonance Imaging (fMRI) data and some applied econometrics problems.
- PhD, Electrical and Computer Engineering, University of British Columbia, 2013