Kaie Kubjas

Kaie Kubjas is an Assistant Professor at Aalto University in Finland. Her research focuses on applying algebraic geometry to statistical and biological problems, with particular expertise in matrix and tensor decompositions. She specializes in nonnegative, positive semidefinite, and subspace-constrained decomposition methods.

Her research bridges pure mathematics and applied sciences, working on theoretical foundations of matrix/tensor factorizations and their practical implementations in data analysis. Current interests include log-concave maximum likelihood estimation and geometric methods for statistical inference.

Dr. Kubjas maintains active collaborations with the Max Planck Institute for Mathematics in the Sciences (MPI Leipzig), notably partnering with Alex Heaton and Stefana Sorea on the project 'Exact Solutions in Log-Concave Maximum Likelihood Estimation'. She previously visited MPI Leipzig in 2017 for workshops and reading groups on algebra-science interfaces.