On this webpage, I've included some descriptions of my research projects.
- Confounder Adjustment with MOUTHWASH: Empirical Bayes with the factor-augmented regression model for confounder adjustment.
- Removing Unwanted Variation: Some of my postdoctoral work on unifying and generalizing confounder adjustment methods.
- Higher-order Spectral Estimators: My work on adaptive higher-order spectral estimation. These estimators generalize a class of popular matrix estimators to tensors. These estimators shrink tensors toward low multilinear-rank estimates.
- The Incredible HOLQ: The incredible higher-order LQ decomposition for tensor data. This generalizes the matrix LQ decomposition to tensors and introduces the concept of a scaled all-orthonormal tensor.
- Equivariant Estimation: A description of my dissertational work on optimal equivariant covariance estimation for tensor datasets.
- Introduction to Tensors: This provides a brief introduction to tensor datasets.