Tselil Schramm (original) (raw)
tselil AT stanford DOT edu
Office: CoDa E254
I am an assistant professor at Stanford in the Department of Statistics (and in Computer Science and Mathematics, by courtesy).
My research is at the intersection of theoretical computer science and statistics. I study algorithms for high-dimensional estimation problems, and I work to characterize and explain information-computation tradeoffs.
Before joining Stanford, I received my PhD from U.C. Berkeley, where I was lucky to be advised byPrasad Raghavendra andSatish Rao. After that I was a postdoc at Harvard and MIT, hosted by the wonderful quadrumvirate of Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo.
Here is a tutorial for pronouncing my name.
**Teaching:
Spring 2025: Intro to Statistics (precalculus) (STATS 60)
Winter 2025: Theory of Statistics II (STATS 300B)
Fall 2024: Machine Learning Theory (STATS 214 / CS 228M)
Winter 2024: Theory of Statistics II (STATS 300B)
Fall 2023: Machine Learning Theory (STATS 214 / CS 228M)
Spring 2023: Probability Theory (STATS 116)
Winter 2023: Intro to Stochastic Processes 1 (STATS 217)
Fall 2022: Machine Learning Theory (STATS 214 / CS 228M)
Spring 2022: The Sum-of-Squares Algorithmic Paradigm in Statistics (STATS 314a)
Winter 2022: Random Processes on Graphs and Lattices (STATS 221)
Spring 2021: Probability Theory (STATS 116)
Winter 2021: The Sum-of-Squares Algorithmic Paradigm in Statistics (STATS 319)
**Selected and Recent Papers [all papers]:
Some easy optimization problems have the overlap-gap property
[arXiv]
withShuangping Li,
in submission.
Discrepancy Algorithms for the Binary Perceptron
[arXiv]
withShuangping Li and Kangjie Zhou,
in submission.
Fast, robust approximate message passing
[arXiv]
with Misha Ivkov,
in submission.
Semidefinite programs simulate approximate message passing robustly
[arXiv]
with Misha Ivkov,
in
STOC 2024
.
Spectral clustering in the Gaussian mixture block model
[arXiv]
withShuangping Li
, in submission.
Local and global expansion in random geometric graphs
[arXiv]
withSiqi Liu,Sidhanth Mohanty, andElizabeth Yang,
in
STOC 2023
.
Testing thresholds for high-dimensional sparse random geometric graphs
[arXiv]
withSiqi Liu,Sidhanth Mohanty, andElizabeth Yang,
in
STOC 2022
.
Invited to the STOC 2022 special issue of SICOMP.
Statistical query algorithms and low-degree tests are almost equivalent
[arXiv]
withMatthew Brennan,Guy Bresler,Sam Hopkins andJerry Li,
in
COLT 2021
, runner up to the Best Paper.
Computational barriers to estimation from low-degree polynomials
[arXiv]
withAlex Wein,
in
The Annals of Statistics, 2022
.
Subexponential LPs approximate max-cut
[arXiv]
withSam Hopkins andLuca Trevisan,
in
FOCS 2020
.
On the power of sum-of-squares for detecting hidden structures
[arXiv]
withSam Hopkins,Pravesh Kothari,Aaron Potechin,Prasad Raghavendra, andDavid Steurer,
in
FOCS 2017
.
Strongly refuting random CSPs below the spectral threshold
[arXiv]
withPrasad RaghavendraandSatish Rao,
in
STOC 2017
.
Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors
[arXiv]
with Sam Hopkins, Jonathan Shi, and David Steurer,
in
STOC 2016
.