Elina Robeva

Elina Robeva is a faculty member in the Department of Mathematics at The University of British Columbia. She obtained her PhD in mathematics at UC Berkeley under the supervision of Bernd Sturmfels in 2016. She then spent 3 years at the MIT mathematics department as a Statistics Instructor and an NSF postdoctoral fellow. During her time at MIT, she won the SIAM Early Career Award in applied algebraic geometry. In 2019, she joined the Department of Mathematics at UBC as an assistant professor. Her research interests include probabilistic graphical models, causality, algebraic statistics, tensor decompositions, super-resolution imaging, density estimation, and applied algebraic geometry. She is also a recipient of the Canada CIFAR AI Chair (2024), the 2023 André Aisenstadt Prize, the 2020 UBC/PIMS Mathematical Sciences Young Faculty Award, and the 2022 CAIMS/PIMS Early Career Researcher Award.

Elina Robeva is an Assistant Professor at The University of British Columbia, specializing in causal inference, graphical models, tensor decomposition, density estimation, and algebraic statistics. Her research explores the intersection of statistics, algebra, and machine learning, with a focus on understanding complex data structures and causal relationships. She has contributed to topics such as tensor decomposition, graphical model duality, and nonnegative rank boundaries. Her work includes developing methods for density estimation, causal discovery, and robust statistical inference. Robeva's research also addresses challenges in high-dimensional data analysis and the geometry of statistical models.