Anna Seigal

Anna Seigal is an Assistant Professor of Applied Mathematics at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Her primary teaching area is Applied Mathematics. She is affiliated with the Computer Science department and focuses on research areas including Applied Algebra and Geometry, Data Science, Machine Learning, Numerical Analysis, and Theory of Computation. Her work bridges mathematics and data science, as evidenced by her recognition with a Sloan Fellowship. She can be contacted via email at aseigal@seas.harvard.edu or through her website at seigal.github.io.

Anna Seigal is an Assistant Professor of Applied Mathematics at Harvard University, specializing in Applied Algebra, Tensors, Applied Algebraic Geometry, and Algebraic Statistics. Her research explores the interplay between algebraic structures and statistical models, with applications in data science and machine learning. She investigates properties of tensors, graphical models, and invariant theory, focusing on problems such as identifiability, maximum likelihood estimation, and geometric interpretations of statistical models. Her work bridges abstract algebra with real-world data analysis, contributing to fields like causal inference, signal processing, and computational biology. Seigal's publications highlight the connections between algebraic geometry and statistical learning, emphasizing the role of symmetry and structure in data representation.