Tselil Schramm

tselil AT mit DOT edu

I am a theoretical computer scientist. My research is in algorithms, and my interests include optimization via convex programs (especially the sum-of-squares hierarchy), and statistical and average-case problems, with a focus on understanding the tradeoff between information and computation.

In January 2021, I will be joining Stanford Statistics as an assistant professor.

Right now, I am visiting the Machine Learning and Optimization group at Microsoft Research.

I received my PhD from U.C. Berkeley, where I was advised by Prasad Raghavendra and Satish 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. I spent Fall 2017 as a Google Research Fellow in the Simons Institute program on Optimization.

Here is a tutorial for pronouncing my name.

Here is a survey on High-dimensional estimation via sum-of-squares proofs that I co-wrote with Prasad Raghavendra and David Steurer, for their ICM 2018 invited lecture.

Subexponential LPs approximate max-cut [arXiv]
with Sam Hopkins and Luca Trevisan, in submission.

(Nearly) efficient algorithms for the graph matching problem on correlated random graphs [arXiv]
with Boaz Barak, Chi-Ning Chou, Zhixian Lei, and Yueqi Sheng, in NeurIPS 2019.

Sherali-Adams strikes back [arXiv]
with Ryan O'Donnell, in CCC 2019.
Invited to the CCC 2019 special issue of Theory of Computing.

A robust spectral algorithm for overcomplete tensor decomposition [PMLR]
with Sam Hopkins, and Jonathan Shi, in COLT 2019.

SOS lower bounds with hard constraints: think global, act local [arXiv]
with Pravesh Kothari, and Ryan O'Donnell, in ITCS 2019.

The threshold for SDP-refutation of random regular NAE-3SAT [arXiv]
with Yash Deshpande, Andrea Montanari, Ryan O'Donnell, and Subhabrata Sen, in SODA 2019.

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.

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).