Tselil Schramm

Tselil Schramm is an Assistant Professor at Stanford University in the Department of Statistics, with courtesy appointments in Computer Science and Mathematics. Her work bridges theoretical computer science and statistics, focusing on algorithm design and information-computation tradeoffs.

Research interests include:

• Theoretical power of sum-of-squares semidefinite programs
• Fast spectral methods for high-dimensional estimation
• Characterizing computational complexity in statistical problems
• Interplay between different computation models

Teaching: She has taught courses like Intro to Statistics, Machine Learning Theory, and The Sum-of-Squares Algorithmic Paradigm in Statistics since joining Stanford in 2022.

Selected publications highlight: Robust algorithmic paradigms for high-dimensional data, computational barriers in low-degree polynomial estimation, and theoretical foundations of statistical query models.

Scientific honors:

• NSF CAREER Award (2020s)
• COLT 2021 Best Paper Runner-up (Statistical query algorithms paper)