# Tselil Schramm

tselil AT seas DOT harv
I am a postdoc in theoretical computer science, at Harvard and MIT.
My hosts are Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo.

During Fall 2017, I was the Google Research Fellow in the Simons Institute program on Discrete and Continuous Optimization.

I am freshly graduated from a PhD in the U.C. Berkeley
Theory Group, where I was advised by
Prasad Raghavendra and
Satish Rao.
I got my B.S. in CS/Math from
Harvey Mudd College,
where Ran Libeskind-Hadas kept me out of trouble.

My research interests include Spectral Algorithms, Spectral Graph Theory, Approximation Algorithms, Semidefinite Programming (especially the Sum-of-Squares Hierarchy), Random Matrices, and more.

Here is a tutorial for pronouncing my name.

Check out the "Intro to sum-of-squares" blog post I wrote for Learning With Errors, Preetum Nakkiran's blog.

**Papers:**

Computing exact minimum cuts without knowing the graph
[arXiv]

with
Aviad Rubinstein and
Matt Weinberg,
in ITCS 2018.

On the power of sum-of-squares for detecting hidden structures
[arXiv]

with
Sam Hopkins,
Pravesh Kothari,
Aaron Potechin,
Prasad Raghavendra, and
David Steurer,
in FOCS 2017.

Fast and robust tensor decomposition with applications to dictionary learning
[arXiv]

with
David Steurer,
in COLT 2017.

Strongly refuting random cSPs below the spectral threshold
[arXiv]

with
Prasad Raghavendra
and
Satish 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.

On the integrality gap of degree-4 sum-of-squares for planted clique

with
Sam Hopkins,
Pravesh Kothari,
Aaron Potechin,
and
Prasad Raghavendra,
in SODA 2016
(merge of [this] paper and [this] paper)

*Invited to the SODA 2016 special issue of ACM Transactions on Algorithms.*

Braess's paradox for the spectral gap in random graphs and delocalization of eigenvectors
[arXiv]

with Ronen Eldan and
Miklos Racz,
in Random Structures & Algorithms (2016).

Near optimal LP rounding algorithms for correlation clustering in complete and complete k-partite graphs
[arXiv]

with Shuchi Chawla,
Konstantin Makarychev,
and Grigory Yaroslavtsev,
in STOC 2015.

Symmetric tensor completion from multilinear entries and learning product mixtures over the hypercube
[arXiv]

with Benjamin Weitz.

Gap amplification for small-set expansion via random walks
[arXiv]

with Prasad Raghavendra,
in APPROX 2014.

Global and local information in clustering labeled block models
[arXiv]

with
Varun Kanade
and
Elchanan Mossel
,
in RANDOM 2014, and in IEEE Transactions on Information Theory (2016).