I am interested in developing innovative statistical methodology for massive data sets with complex dependence structures, including functional, time series, and spatial data. For many applications, these dependence structures appear concurrently.
I prefer hierarchically Bayesian models, which provide both sufficient model flexibility to tackle complex problems as well as mechanisms for regularization to prevent overfitting.
With my research, I seek to directly and meaningfully address open questions in important fields such as economics, neuroscience, biomedical engineering, finance, and astronomy.
- ASA Business and Economic Statistics Section Best Student Paper Award: Dynamic Shrinkage Processes (1/15/2018)
- CMStatistics 2017 Presented Functional Autoregression for Sparsely Sampled Data in a special section on recent developments in functional time series analysis (12/16/2017 - 12/18/2017)
- Joint Statistical Meetings: Presented Functional Autoregression for Sparsely Sampled Data in a special section for ASA Nonparametrics Section Student Paper Award winners and served as session chair for Bayes Theory and Foundations (7/31/2017 - 8/2/2017)
- An R package for Dynamic Shrinkage Processes is available on GitHub! (7/25/2017)
Install and load using the following code:
- Curve-fitting of irregular data via Bayesian trend filtering: btf(...)
- An adaptive time-varying parameter regression model: btf_reg(...)
- Curve-fitting of irregular data with unequally-spaced observations: btf_bpsline(...)
- PhD dissertation submitted and approved: Bayesian Methods for Functional and Time Series Data, Cornell University, Department of Statistical Science (7/13/2017)
- Dynamic Shrinkage Processes submitted! (7/3/2017)
- An R package for A Bayesian multivariate functional dynamic linear model is available on GitHub! (5/17/2017)
- ASA Nonparametrics Section Student Paper Award: Functional autoregression for sparsely sampled data (3/16/2017)