Aaron J. Molstad

I am an Associate Professor in the School of Statistics at the University of Minnesota. I also hold a graduate faculty appointment in Data Science. My primary research interests are in multivariate analysis, numerical optimization, statistical genetics and genomics, and more broadly, statistical and machine learning. Please see my research page for articles and funding information. I serve as Associate Editor for Data Science in Science and Journal of Computational and Graphical Statistics.

Recent and upcoming talks

(12/25) DSI Machine Learning Seminar in Minneapolis, MN
(7/25) ASA NSF@75 Virtual Conference
(5/25) STATGEN 2025 in Minneapolis, MN
(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

(11/25) A unified approach to spatial domain detection and cell-type deconvolution in spot-based spatial transcriptomics available on arXiv
(9/25) The why and how of convex clustering accepted by Annual Review of Statistics and its Applications
(9/25) Universal inference for variance components now available on arXiv
(9/25) Promoted to Associate Professor with tenure
(8/25) Conditional probability tensor decompositions for multivariate categorical response regression accepted by JASA
(7/25) Jinwen Fu's paper, A direct approach to tree-guided feature aggregation for high-dimensional regression now available on arXiv
(7/25) Dr. Yiqiao Zhang successfully defended her Ph.D. thesis. In a few weeks, she will join Microsoft in Seattle. Congratulations Dr. Zhang!
(7/25) Review paper on convex clustering now available on arXiv
(7/25) Dr. Karina Gelis-Cadena successfully defended her Ph.D. thesis. She will start as Lecturer at University of Florida in the fall. Congratulations Dr. Gelis-Cadena!
(6/25) Yisen Jin's papers, Smooth and shape-constrained quantile distributed lag models and Kernelized discriminant analysis for joint modeling of multivariate categorical responses accepted at Biometrics and JCGS, respectively
(3/25) Dr. Yisen Jin successfully defended his Ph.D. thesis. In a few weeks, he will join Microsoft in Seattle. Congratulations Dr. Jin!
(1/25) Fast and reliable confidence intervals for a variance component accepted at Biometrika
(1/25) Hyun Jung Koo received the DSI-MnDRIVE Graduate Assistantship Award. Congratulations HJ!
(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