Yang Chen is currently an assistant professor at the Department of Statistics at the University of Michigan and a research assistant professor at Michigan Institute for Data Science (MIDAS). Yang's research interests are statistical methodology and computational algorithms motivated by scientific applications. Her past and ongoing projects are focused on the following topics:
- Bayesian modeling and computation
- Hidden Markov models and general finite mixture models
- Applied statistics and machine learning in astronomy and space science
- Statistical methods for large-scale imaging data
- Spatial-temporal data analysis
- Uncertainty quantification for complex models
Full bio at: https://yangchenfunstatistics.github.io/yangchen.github.io//
Talk: Video Imputation and Prediction Methods with Applications in Space Weather
Abstract: The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and a satellite. This delay can result in a GPS positioning error. Thus, it is important to monitor and forecast the TEC maps. However, the observed TEC maps have big patches of missingness in the ocean and scattered small areas on the land. Thus, precise imputation and prediction of the TEC maps are crucial in space weather forecasting.
In this talk, I first present several extensions of existing matrix completion algorithms to achieve TEC map reconstruction, accounting for spatial smoothness and temporal consistency while preserving essential structures of the TEC maps. We show that our proposed method achieves better reconstructed TEC maps as compared to existing methods in the literature. I will also briefly describe the use of our large-scale complete TEC database. Then, I present a new model for forecasting time series data distributed on a matrix-shaped spatial grid, using the historical spatiotemporal data and auxiliary vector-valued time series data. Large sample asymptotics of the estimators for both finite and high dimensional settings are established. Performances of the model are validated with extensive simulation studies and an application to forecast the global TEC distributions.