Aaron J. Molstad

I am an Assistant Professor in the School of Statistics at the University of Minnesota. My primary research interests are in multivariate analysis, numerical optimization, statistical genetics and genomics, and more broadly, statistical and machine learning. Recently, I have been especially interested in single cell and spatial transcriptomic data analysis: see my research page for details. I serve as Associate Editor for Data Science in Science and Journal of Computational and Graphical Statistics.

Recent and upcoming talks

(11/24) KU Probability and Statistics Conference in Lawrence, KS
(11/24) ASA SLDS Conference in Newport Beach, CA
(8/24) Joint Statistical Meeting in Portland, OR
(7/24) Model-based Clustering Working Group in Bertinoro, Italy
(5/24) SIAM Conference on Applied Linear Algebra in Paris

News and updates

(11/24) Multi-response linear discriminant analysis in high dimensions accepted at Journal of Machine Learning Research
(10/24) Hongru Zhao's paper, Subspace decompositions for association structure learning in multivariate categorical response regression now available on arXiv
(8/24) Yisen Jin's paper, Smooth and shape-constrained quantile distributed lag models now available on arXiv
(7/24) Heterogeneity-aware integrative regression for ancestry-specific association studies accepted at Biometrics
(7/24) Objective and reliable methods for inference with modern omics data grant awarded by the National Science Foundation (DMS-2413294): this grant will fund our work on methods for analyzing spatially-resolved and single-cell transcriptomic data
(4/24) Fast and reliable confidence intervals for a variance component or proportion now available on arXiv. I will present this work at JSM 2024
(4/24) Direct covariance matrix estimation with compositional data accepted at EJS