Area of research
Statistics and Probability · Artificial Intelligence
Research interest
Research interests include Statistical Methods and Inference, Bayesian Methods and Mixture Models, Statistical Methods and Bayesian Inference, and Sparse and Compressive Sensing Techniques.
Conformal prediction beyond exchangeability
Derandomised knockoffs: leveraging <i>e</i>-values for false discovery rate control
Permutation Tests Using Arbitrary Permutation Distributions
A power analysis for model-X knockoffs with ℓp-regularized statistics
Addressing CT metal artifacts using photon‐counting detectors and one‐step spectral CT image reconstruction
Half-trek criterion for identifiability of latent variable models
Fast and flexible estimation of effective migration surfaces
The limits of distribution-free conditional predictive inference
Between hard and soft thresholding: optimal iterative thresholding algorithms
Estimating the Spectrum in Computed Tomography Via Kullback–Leibler Divergence Constrained Optimization
e-Publications@Marquette (Marquette University) 2019cited by 32position: middle
Contraction and uniform convergence of isotonic regression
Multiple Testing with the Structure-Adaptive Benjamini–Hochberg Algorithm
Gradient descent with non-convex constraints: local concavity determines convergence
A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data
The function-on-scalar LASSO with applications to longitudinal GWAS
An algorithm for constrained one-step inversion of spectral CT data
EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
Accumulation Tests for FDR Control in Ordered Hypothesis Testing
The<i>p</i>-filter: Multilayer False Discovery Rate Control for Grouped Hypotheses
Inferring skeletal production from time-averaged assemblages: skeletal loss pulls the timing of production pulses towards the modern period
Long-term accumulation of carbonate shells reflects a 100-fold drop in loss rate