Elina Robeva

Elina Robeva serves as an Assistant Professor in the Department of Mathematics at the University of British Columbia (UBC) in Vancouver, Canada, specializing in algebraic statistics with focus on graphical models, total positivity, and tensor decompositions. Her work explores combinatorial and algebraic structures in statistical and machine learning problems, including teaching specialized courses like the Fall 2020 online topics course on tensor decompositions.

Education:

• PhD in Mathematics from UC Berkeley (2016), supervised by Bernd Sturmfels

Research Focus: Robeva's scholarship bridges abstract algebra with practical statistical applications, emphasizing how combinatorial frameworks reveal hidden patterns in complex data. Her investigations into tensor decompositions advance machine learning methodologies, while work on graphical models and total positivity contributes to probabilistic reasoning and optimization theory. This interdisciplinary approach positions algebraic statistics as a critical tool for modern data science challenges.

Affiliation History: Listed as a Nonlinear Algebra group member at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) from 2017-2022, she visited MPI Leipzig in summer 2018 for the real algebraic geometry summer school, indicating sustained collaborative ties with the institute despite her primary UBC appointment.

Professional Activities: Actively engaged in knowledge dissemination through specialized coursework and research outreach, with no documented advising roles or grant activities in the provided materials. Her academic trajectory reflects a commitment to advancing algebraic methods in statistical theory and applications.