1 citations · 1 across the 5 of their papers we have counts for
5 papers
Supervised Low-Rank Structure Discovery for Developmental Epigenetic Aging in Ultra-High-Dimensional DNA Methylation Data
Priyam Das, Jiyeon Song, Lathika Mohanraj +3
Ultra-high-dimensional array-based CpG methylation studies require statistical frameworks that simultaneously provide supervised structure discovery, interpretability, scalable lat…
BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference
Beomchang Kim, Subhrajyoty Roy, Priyam Das
Optimization over the Stiefel manifold , the set of column-orthonormal matrices, is fundamental in statistics, machine learning, and scientific compu…
Bayesian Univariate-Guided Sparse Regression for Ultra-High-Dimensional Data
Priyam Das
Ultra-high-dimensional genomic studies require simultaneous identification of sparse signals, control of spurious discoveries, and computationally feasible inference when the numbe…
BLOC: A Global Optimization Framework for Sparse Covariance Estimation with Non-Convex Penalties
Priyam Das, Trambak Banerjee, Prajamitra Bhuyan
We introduce BLOC (Black-box Optimization over Correlation matrices), a general framework for sparse covariance estimation with non-convex penalties. BLOC operates on the manifold…
SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model
Beomchang Kim, Zongqi Xia, Priyam Das
Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heteroge…